Algorithms¶
All algorithm are derived from nnabla_rl.algorithm.Algorithm
.
Note
Algorithm will run on cpu by default (No matter what nnabla context is set in prior to the instantiation). If you want to run the algorithm on gpu, set the gpu_id through the algorithm’s config. Note that the algorithm will override the nnabla context when the training starts.
Algorithm¶
- class nnabla_rl.algorithm.AlgorithmConfig(gpu_id: int = - 1)[source]¶
List of algorithm common configuration
- Parameters
gpu_id (int) – id of the gpu to use. If negative, the training will run on cpu. Defaults to -1.
- class nnabla_rl.algorithm.Algorithm(env_info, config=AlgorithmConfig(gpu_id=- 1))[source]¶
Base Algorithm class
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – : environment or environment infoconfig (
AlgorithmConfig
) – configuration of the algorithm
Note
Default functions, solvers and configurations are set to the configurations of each algorithm’s original paper. Default functions may not work depending on the environment.
- abstract compute_eval_action(state, *, begin_of_episode=False) numpy.ndarray [source]¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_rnn_supported() bool [source]¶
Check whether the algorithm supports rnn models or not
- Returns
True if the algorithm supports rnn models. Otherwise False.
- Return type
bool
- abstract classmethod is_supported_env(env_or_env_info: Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo]) bool [source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property iteration_num: int¶
Current iteration number.
- Returns
Current iteration number of running training.
- Return type
int
- property latest_iteration_state: Dict[str, Any]¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
- set_hooks(hooks: Sequence[nnabla_rl.hook.Hook])[source]¶
Set hooks for running additional operation during training. Previously set hooks will be removed and replaced with new hooks.
- Parameters
hooks (list of nnabla_rl.hook.Hook) – Hooks to invoke during training
- train(env_or_buffer: Union[gym.core.Env, nnabla_rl.replay_buffer.ReplayBuffer], total_iterations: int = 9223372036854775807)[source]¶
Train the policy with reinforcement learning algorithm
- Parameters
env_or_buffer (Union[gym.Env, ReplayBuffer]) – Target environment to train the policy online or reply buffer to train the policy offline.
total_iterations (int) – Total number of iterations to train the policy.
- Raises
UnsupportedTrainingException – Raises if this algorithm does not support the training method for given parameter.
- train_offline(replay_buffer: gym.core.Env, total_iterations: int = 9223372036854775807)[source]¶
Train the policy using only the replay buffer.
- Parameters
replay_buffer (ReplayBuffer) – Replay buffer to sample experiences to train the policy.
total_iterations (int) – Total number of iterations to train the policy.
- Raises
UnsupportedTrainingException – Raises if the algorithm does not support offline training
- train_online(train_env: gym.core.Env, total_iterations: int = 9223372036854775807)[source]¶
Train the policy by interacting with given environment.
- Parameters
train_env (gym.Env) – Target environment to train the policy.
total_iterations (int) – Total number of iterations to train the policy.
- Raises
UnsupportedTrainingException – Raises if the algorithm does not support online training
A2C¶
- class nnabla_rl.algorithms.a2c.A2CConfig(gpu_id: int = - 1, gamma: float = 0.99, n_steps: int = 5, learning_rate: float = 0.0007, entropy_coefficient: float = 0.01, value_coefficient: float = 0.5, decay: float = 0.99, epsilon: float = 1e-05, start_timesteps: int = 1, actor_num: int = 8, timelimit_as_terminal: bool = False, max_grad_norm: Optional[float] = 0.5, seed: int = - 1, learning_rate_decay_iterations: int = 50000000)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for A2C algorithm
- Parameters
gamma (float) – discount factor of rewards. Defaults to 0.99.
n_steps (int) – number of rollout steps. Defaults to 5.
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.0007.entropy_coefficient (float) – scalar of entropy regularization term. Defaults to 0.01.
value_coefficient (float) – scalar of value loss. Defaults to 0.5.
decay (float) – decay parameter of Adam solver. Defaults to 0.99.
epsilon (float) – epislon of Adam solver. Defaults to 0.00001.
start_timesteps (int) – the timestep when training starts. The algorithm will collect experiences from the environment by acting randomly until this timestep. Defaults to 1.
actor_num (int) – number of parallel actors. Defaults to 8.
timelimit_as_terminal (bool) – Treat as done if the environment reaches the timelimit. Defaults to False.
max_grad_norm (float) – threshold value for clipping gradient. Defaults to 0.5.
seed (int) – base seed of random number generator used by the actors. Defaults to 1.
learning_rate_decay_iterations (int) – learning rate will be decreased lineary to 0 till this iteration number. If 0 or negative, learning rate will be kept fixed. Defaults to 50000000.
- class nnabla_rl.algorithms.a2c.A2C(env_or_env_info, v_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.v_function.VFunction] = <nnabla_rl.algorithms.a2c.DefaultVFunctionBuilder object>, v_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.a2c.DefaultSolverBuilder object>, policy_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.policy.StochasticPolicy] = <nnabla_rl.algorithms.a2c.DefaultPolicyBuilder object>, policy_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.a2c.DefaultSolverBuilder object>, config=A2CConfig(gpu_id=-1, gamma=0.99, n_steps=5, learning_rate=0.0007, entropy_coefficient=0.01, value_coefficient=0.5, decay=0.99, epsilon=1e-05, start_timesteps=1, actor_num=8, timelimit_as_terminal=False, max_grad_norm=0.5, seed=-1, learning_rate_decay_iterations=50000000))[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
Advantage Actor-Critic (A2C) algorithm implementation.
This class implements the Advantage Actor-Critic (A2C) algorithm. A2C is the synchronous version of A3C, Asynchronous Advantage Actor-Critic. A3C was proposed by V. Mnih, et al. in the paper: “Asynchronous Methods for Deep Reinforcement Learning” For detail see: https://arxiv.org/abs/1602.01783
This algorithm only supports online training.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infov_function_builder (
ModelBuilder[VFunction]
) – builder of v function modelsv_solver_builder (
SolverBuilder
) – builder for v function solverspolicy_builder (
ModelBuilder[StochasicPolicy]
) – builder of policy modelspolicy_solver_builder (
SolverBuilder
) – builder for policy solversconfig (
A2CConfig
) – configuration of A2C algorithm
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
ATRPO¶
- class nnabla_rl.algorithms.atrpo.ATRPOConfig(gpu_id: int = - 1, lmb: float = 0.95, num_steps_per_iteration: int = 5000, pi_batch_size: int = 5000, sigma_kl_divergence_constraint: float = 0.01, maximum_backtrack_numbers: int = 10, backtrack_coefficient: float = 0.8, conjugate_gradient_damping: float = 0.01, conjugate_gradient_iterations: int = 10, vf_epochs: int = 5, vf_batch_size: int = 64, vf_learning_rate: float = 0.00030000000000000003, vf_l2_reg_coefficient: float = 0.003, preprocess_state: bool = True, gpu_batch_size: Optional[int] = None, learning_rate_decay_iterations: int = 10000000)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for Average Reward TRPO algorithm
- Parameters
lmb (float) – Scalar of lambda return’s computation in GAE. Defaults to 0.95. This configuration is related to bias and variance of estimated value. If it is close to 0, estimated value is low-variance but biased. If it is close to 1, estimated value is unbiased but high-variance.
num_steps_per_iteration (int) – Number of steps per each training iteration for collecting on-policy experinces. Increasing this step size is effective to get precise parameters of policy and value function updating, but computational time of each iteration will increase. Defaults to 5000.
pi_batch_size (int) – Trainig batch size of policy. Usually, pi_batch_size is the same as num_steps_per_iteration. Defaults to 5000.
sigma_kl_divergence_constraint (float) – Constraint size of kl divergence between previous policy and updated policy. Defaults to 0.01.
maximum_backtrack_numbers (int) – Maximum backtrack numbers of linesearch. Defaults to 10.
backtrack_coefficient (float) – Coefficient value of linesearch. Defaults to 0.8.
conjugate_gradient_damping (float) – Damping size of conjugate gradient method. Defaults to 0.01.
conjugate_gradient_iterations (int) – Number of iterations of conjugate gradient method. Defaults to 10.
vf_epochs (int) – Number of epochs in each iteration. Defaults to 5.
vf_batch_size (int) – Training batch size of value function. Defaults to 64.
vf_learning_rate (float) – Learning rate which is set to the solvers of value function. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 3. * 1e-4.vf_l2_reg_coefficient (float) – L2 regulization coefficient for the network parameters. Defaults to 3 * 1e-3
preprocess_state (bool) – Enable preprocessing the states in the collected experiences before feeding as training batch. Defaults to True.
gpu_batch_size (int, optional) – Actual batch size to reduce one forward gpu calculation memory. As long as gpu memory size is enough, this configuration should not be specified. If not specified, gpu_batch_size is the same as pi_batch_size. Defaults to None.
learning_rate_decay_iterations (int) – learning rate will be decreased lineary to 0 till this iteration number. If 0 or negative, learning rate will be kept fixed. Defaults to 10000000.
- class nnabla_rl.algorithms.atrpo.ATRPO(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.atrpo.ATRPOConfig = ATRPOConfig(gpu_id=-1, lmb=0.95, num_steps_per_iteration=5000, pi_batch_size=5000, sigma_kl_divergence_constraint=0.01, maximum_backtrack_numbers=10, backtrack_coefficient=0.8, conjugate_gradient_damping=0.01, conjugate_gradient_iterations=10, vf_epochs=5, vf_batch_size=64, vf_learning_rate=0.00030000000000000003, vf_l2_reg_coefficient=0.003, preprocess_state=True, gpu_batch_size=None, learning_rate_decay_iterations=10000000), v_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.v_function.VFunction] = <nnabla_rl.algorithms.atrpo.DefaultVFunctionBuilder object>, v_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.atrpo.DefaultSolverBuilder object>, policy_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.policy.StochasticPolicy] = <nnabla_rl.algorithms.atrpo.DefaultPolicyBuilder object>, state_preprocessor_builder: typing.Optional[nnabla_rl.builders.preprocessor_builder.PreprocessorBuilder] = <nnabla_rl.algorithms.atrpo.DefaultPreprocessorBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.atrpo.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
On-Policy Deep Reinforcement Learning for the Average-Reward Criterion implementation.
This class implements the Average Reward Trust Region Policy Optimiation (ATRPO) with Generalized Advantage Estimation (GAE) algorithm proposed by Yiming Zhang, et al. and J. Schulman, et al. in the paper: “On-Policy Deep Reinforcement Learning for the Average-Reward Criterion” and “High-Dimensional Continuous Control Using Generalized Advantage Estimation” For detail see: https://arxiv.org/abs/2106.07329 and https://arxiv.org/abs/1506.02438
This algorithm only supports online training.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
PPOConfig
) – configuration of TRPO algorithmv_function_builder (
ModelBuilder[VFunction]
) – builder of v function modelsv_solver_builder (
SolverBuilder
) – builder for v function solverspolicy_builder (
ModelBuilder[StochasicPolicy]
) – builder of policy modelsstate_preprocessor_builder (None or
PreprocessorBuilder
) – state preprocessor builder to preprocess the statesexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
BCQ¶
- class nnabla_rl.algorithms.bcq.BCQConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 0.001, batch_size: int = 100, tau: float = 0.005, lmb: float = 0.75, phi: float = 0.05, num_q_ensembles: int = 2, num_action_samples: int = 10)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for BCQ algorithm
- Parameters
gamma (float) – discount factor of reward. Defaults to 0.99.
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.001.batch_size (int) – training batch size. Defaults to 100.
tau (float) – target network’s parameter update coefficient. Defaults to 0.005.
lmb (float) – weight \(\lambda\) used for balancing the ratio between \(\min{Q}\) and \(\max{Q}\) on target q value generation (i.e. \(\lambda\min{Q} + (1 - \lambda)\max{Q}\)). Defaults to 0.75.
phi (float) – action perturbator noise coefficient. Defaults to 0.05.
num_q_ensembles (int) – number of q function ensembles . Defaults to 2.
num_action_samples (int) – number of actions to sample for computing target q values. Defaults to 10.
- class nnabla_rl.algorithms.bcq.BCQ(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.bcq.BCQConfig = BCQConfig(gpu_id=-1, gamma=0.99, learning_rate=0.001, batch_size=100, tau=0.005, lmb=0.75, phi=0.05, num_q_ensembles=2, num_action_samples=10), q_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.q_function.QFunction] = <nnabla_rl.algorithms.bcq.DefaultQFunctionBuilder object>, q_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.bcq.DefaultSolverBuilder object>, vae_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.encoder.VariationalAutoEncoder] = <nnabla_rl.algorithms.bcq.DefaultVAEBuilder object>, vae_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.bcq.DefaultSolverBuilder object>, perturbator_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.perturbator.Perturbator] = <nnabla_rl.algorithms.bcq.DefaultPerturbatorBuilder object>, perturbator_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.bcq.DefaultSolverBuilder object>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
Batch-Constrained Q-learning (BCQ) algorithm
This class implements the Batch-Constrained Q-learning (BCQ) algorithm proposed by S. Fujimoto, et al. in the paper: “Off-Policy Deep Reinforcement Learning without Exploration” For details see: https://arxiv.org/abs/1812.02900
This algorithm only supports offline training.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
BCQConfig
) – configuration of the BCQ algorithmq_function_builder (
ModelBuilder[QFunction]
) – builder of q-function modelsq_solver_builder (
SolverBuilder
) – builder for q-function solversvae_builder (
ModelBuilder[VariationalAutoEncoder]
) – builder of variational auto encoder modelsvae_solver_builder (
SolverBuilder
) – builder for variational auto encoder solversperturbator_builder (
PerturbatorBuilder
) – builder of perturbator modelsperturbator_solver_builder (
SolverBuilder
) – builder for perturbator solvers
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
BEAR¶
- class nnabla_rl.algorithms.bear.BEARConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 0.001, batch_size: int = 100, tau: float = 0.005, lmb: float = 0.75, epsilon: float = 0.05, num_q_ensembles: int = 2, num_mmd_actions: int = 5, num_action_samples: int = 10, mmd_type: str = 'gaussian', mmd_sigma: float = 20.0, initial_lagrange_multiplier: Optional[float] = None, fix_lagrange_multiplier: bool = False, warmup_iterations: int = 20000, use_mean_for_eval: bool = False)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for BEAR algorithm.
- Parameters
gamma (float) – discount factor of rewards. Defaults to 0.99.
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.001.batch_size (int) – training batch size. Defaults to 100.
tau (float) – target network’s parameter update coefficient. Defaults to 0.005.
lmb (float) – weight \(\lambda\) used for balancing the ratio between \(\min{Q}\) and \(\max{Q}\) on target q value generation (i.e. \(\lambda\min{Q} + (1 - \lambda)\max{Q}\)). Defaults to 0.75.
epsilon (float) – inequality constraint of dual gradient descent. Defaults to 0.05.
num_q_ensembles (int) – number of q ensembles . Defaults to 2.
num_mmd_actions (int) – number of actions to sample for computing maximum mean discrepancy (MMD). Defaults to 5.
num_action_samples (int) – number of actions to sample for computing target q values. Defaults to 10.
mmd_type (str) – kernel type used for MMD computation. laplacian or gaussian is supported. Defaults to gaussian.
mmd_sigma (float) – parameter used for adjusting the MMD. Defaults to 20.0.
initial_lagrange_multiplier (float, optional) – Initial value of lagrange multiplier. If not specified, random value sampled from normal distribution will be used instead.
fix_lagrange_multiplier (bool) – Either to fix the lagrange multiplier or not. Defaults to False.
warmup_iterations (int) – Number of iterations until start updating the policy. Defaults to 20000
use_mean_for_eval (bool) – Use mean value instead of best action among the samples for evaluation. Defaults to False.
- class nnabla_rl.algorithms.bear.BEAR(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.bear.BEARConfig = BEARConfig(gpu_id=-1, gamma=0.99, learning_rate=0.001, batch_size=100, tau=0.005, lmb=0.75, epsilon=0.05, num_q_ensembles=2, num_mmd_actions=5, num_action_samples=10, mmd_type='gaussian', mmd_sigma=20.0, initial_lagrange_multiplier=None, fix_lagrange_multiplier=False, warmup_iterations=20000, use_mean_for_eval=False), q_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.q_function.QFunction] = <nnabla_rl.algorithms.bear.DefaultQFunctionBuilder object>, q_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.bear.DefaultSolverBuilder object>, pi_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.policy.StochasticPolicy] = <nnabla_rl.algorithms.bear.DefaultPolicyBuilder object>, pi_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.bear.DefaultSolverBuilder object>, vae_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.encoder.VariationalAutoEncoder] = <nnabla_rl.algorithms.bear.DefaultVAEBuilder object>, vae_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.bear.DefaultSolverBuilder object>, lagrange_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.bear.DefaultSolverBuilder object>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
Bootstrapping Error Accumulation Reduction (BEAR) algorithm.
This class implements the Bootstrapping Error Accumulation Reduction (BEAR) algorithm proposed by A. Kumar, et al. in the paper: “Stabilizing Off-Policy Q-learning via Bootstrapping Error Reduction” For details see: https://arxiv.org/abs/1906.00949
This algorithm only supports offline training.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
BEARConfig
) – configuration of the BEAR algorithmq_function_builder (
ModelBuilder[QFunction]
) – builder of q-function modelsq_solver_builder (
SolverBuilder
) – builder for q-function solverspi_function_builder (
ModelBuilder[StochasticPolicy]
) – builder of policy modelspi_solver_builder (
SolverBuilder
) – builder for policy solversvae_builder (
ModelBuilder[VariationalAutoEncoder]
) – builder of variational auto encoder modelsvae_solver_builder (
SolverBuilder
) – builder for variational auto encoder solverslagrange_solver_builder (
SolverBuilder
) – builder for lagrange multiplier solver
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
Categorical DDQN¶
- class nnabla_rl.algorithms.categorical_ddqn.CategoricalDDQNConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 0.00025, batch_size: int = 32, num_steps: int = 1, start_timesteps: int = 50000, replay_buffer_size: int = 1000000, learner_update_frequency: int = 4, target_update_frequency: int = 10000, max_explore_steps: int = 1000000, initial_epsilon: float = 1.0, final_epsilon: float = 0.01, test_epsilon: float = 0.001, v_min: float = - 10.0, v_max: float = 10.0, num_atoms: int = 51, loss_reduction_method: str = 'mean', unroll_steps: int = 1, burn_in_steps: int = 0, reset_rnn_on_terminal: bool = True)[source]¶
Bases:
nnabla_rl.algorithms.categorical_dqn.CategoricalDQNConfig
- class nnabla_rl.algorithms.categorical_ddqn.CategoricalDDQN(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.categorical_dqn.CategoricalDQNConfig = CategoricalDDQNConfig(gpu_id=-1, gamma=0.99, learning_rate=0.00025, batch_size=32, num_steps=1, start_timesteps=50000, replay_buffer_size=1000000, learner_update_frequency=4, target_update_frequency=10000, max_explore_steps=1000000, initial_epsilon=1.0, final_epsilon=0.01, test_epsilon=0.001, v_min=-10.0, v_max=10.0, num_atoms=51, loss_reduction_method='mean', unroll_steps=1, burn_in_steps=0, reset_rnn_on_terminal=True), value_distribution_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.distributional_function.ValueDistributionFunction] = <nnabla_rl.algorithms.categorical_dqn.DefaultValueDistFunctionBuilder object>, value_distribution_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.categorical_dqn.DefaultSolverBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.categorical_dqn.DefaultReplayBufferBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.categorical_dqn.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithms.categorical_dqn.CategoricalDQN
Categorical Double DQN algorithm.
This class implements the Categorical Double DQN algorithm introduced by M. Bellemare, et al. in the paper: “Rainbow: Combining Improvements in Deep Reinforcement Learning” For details see: https://arxiv.org/abs/1710.02298. The difference between Categorical DQN and this algorithm is the update target of q-value. This algorithm uses following double DQN style q-value target for Categorical Q value update. \(r + \gamma Q_{\text{target}}(s_{t+1}, \arg\max_{a}{Q(s_{t+1}, a)})\).
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
CategoricalDDQNConfig
) – configuration of the CategoricalDDQN algorithmvalue_distribution_builder (
ModelBuilder[ValueDistributionFunctionFunction]
) – builder of value distribution function modelsvalue_distribution_solver_builder (
SolverBuilder
) – builder of value distribution function solversreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_bufferexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
Categorical DQN¶
- class nnabla_rl.algorithms.categorical_dqn.CategoricalDQNConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 0.00025, batch_size: int = 32, num_steps: int = 1, start_timesteps: int = 50000, replay_buffer_size: int = 1000000, learner_update_frequency: int = 4, target_update_frequency: int = 10000, max_explore_steps: int = 1000000, initial_epsilon: float = 1.0, final_epsilon: float = 0.01, test_epsilon: float = 0.001, v_min: float = - 10.0, v_max: float = 10.0, num_atoms: int = 51, loss_reduction_method: str = 'mean', unroll_steps: int = 1, burn_in_steps: int = 0, reset_rnn_on_terminal: bool = True)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for CategoricalDQN algorithm.
- Parameters
gamma (float) – discount factor of rewards. Defaults to 0.99.
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.001.batch_size (int) – training batch size. Defaults to 32.
num_steps (int) – number of steps for N-step Q targets. Defaults to 1.
start_timesteps (int) – the timestep when training starts. The algorithm will collect experiences from the environment by acting randomly until this timestep. Defaults to 50000.
replay_buffer_size (int) – the capacity of replay buffer. Defaults to 1000000.
learner_update_frequency (float) – the interval of learner update. Defaults to 4
target_update_frequency (float) – the interval of target q-function update. Defaults to 10000.
max_explore_steps (int) – the number of steps decaying the epsilon value. The epsilon will be decayed linearly \(\epsilon=\epsilon_{init} - step\times\frac{\epsilon_{init} - \epsilon_{final}}{max\_explore\_steps}\). Defaults to 1000000.
initial_epsilon (float) – the initial epsilon value for ε-greedy explorer. Defaults to 1.0.
final_epsilon (float) – the last epsilon value for ε-greedy explorer. Defaults to 0.01.
test_epsilon (float) – the epsilon value on testing. Defaults to 0.001.
v_min (float) – lower limit of the value used in value distribution function. Defaults to -10.0.
v_max (float) – upper limit of the value used in value distribution function. Defaults to 10.0.
num_atoms (int) – the number of bins used in value distribution function. Defaults to 51.
loss_reduction_method (str) – KL loss reduction method. “sum” or “mean” is supported. Defaults to mean.
unroll_steps (int) – Number of steps to unroll tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
burn_in_steps (int) – Number of burn-in steps to initiaze recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
reset_rnn_on_terminal (bool) – Reset recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to True.
- class nnabla_rl.algorithms.categorical_dqn.CategoricalDQN(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.categorical_dqn.CategoricalDQNConfig = CategoricalDQNConfig(gpu_id=-1, gamma=0.99, learning_rate=0.00025, batch_size=32, num_steps=1, start_timesteps=50000, replay_buffer_size=1000000, learner_update_frequency=4, target_update_frequency=10000, max_explore_steps=1000000, initial_epsilon=1.0, final_epsilon=0.01, test_epsilon=0.001, v_min=-10.0, v_max=10.0, num_atoms=51, loss_reduction_method='mean', unroll_steps=1, burn_in_steps=0, reset_rnn_on_terminal=True), value_distribution_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.distributional_function.ValueDistributionFunction] = <nnabla_rl.algorithms.categorical_dqn.DefaultValueDistFunctionBuilder object>, value_distribution_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.categorical_dqn.DefaultSolverBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.categorical_dqn.DefaultReplayBufferBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.categorical_dqn.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
Categorical DQN algorithm.
This class implements the Categorical DQN algorithm proposed by M. Bellemare, et al. in the paper: “A Distributional Perspective on Reinfocement Learning” For details see: https://arxiv.org/abs/1707.06887
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
CategoricalDQNConfig
) – configuration of the CategoricalDQN algorithmvalue_distribution_builder (
ModelBuilder[ValueDistributionFunctionFunction]
) – builder of value distribution function modelsvalue_distribution_solver_builder (
SolverBuilder
) – builder of value distribution function solversreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_bufferexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_rnn_supported()[source]¶
Check whether the algorithm supports rnn models or not
- Returns
True if the algorithm supports rnn models. Otherwise False.
- Return type
bool
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
DDPG¶
- class nnabla_rl.algorithms.ddpg.DDPGConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 0.001, batch_size: int = 100, tau: float = 0.005, start_timesteps: int = 10000, replay_buffer_size: int = 1000000, exploration_noise_sigma: float = 0.1, num_steps: int = 1, actor_unroll_steps: int = 1, actor_burn_in_steps: int = 0, actor_reset_rnn_on_terminal: bool = True, critic_unroll_steps: int = 1, critic_burn_in_steps: int = 0, critic_reset_rnn_on_terminal: bool = True)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for DDPG algorithm
- Parameters
gamma (float) – discount factor of rewards. Defaults to 0.99.
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.001.batch_size (int) – training batch size. Defaults to 100.
tau (float) – target network’s parameter update coefficient. Defaults to 0.005.
start_timesteps (int) – the timestep when training starts. The algorithm will collect experiences from the environment by acting randomly until this timestep. Defaults to 10000.
replay_buffer_size (int) – capacity of the replay buffer. Defaults to 1000000.
exploration_noise_sigma (float) – standard deviation of gaussian exploration noise. Defaults to 0.1.
num_steps (int) – number of steps for N-step Q targets. Defaults to 1.
actor_unroll_steps (int) – Number of steps to unroll actor’s tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
actor_burn_in_steps (int) – Number of burn-in steps to initiaze actor’s recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
actor_reset_rnn_on_terminal (bool) – Reset actor’s recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to False.
critic_unroll_steps (int) – Number of steps to unroll critic’s tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
critic_burn_in_steps (int) – Number of burn-in steps to initiaze critic’s recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
critic_reset_rnn_on_terminal (bool) – Reset critic’s recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to False.
- class nnabla_rl.algorithms.ddpg.DDPG(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.ddpg.DDPGConfig = DDPGConfig(gpu_id=-1, gamma=0.99, learning_rate=0.001, batch_size=100, tau=0.005, start_timesteps=10000, replay_buffer_size=1000000, exploration_noise_sigma=0.1, num_steps=1, actor_unroll_steps=1, actor_burn_in_steps=0, actor_reset_rnn_on_terminal=True, critic_unroll_steps=1, critic_burn_in_steps=0, critic_reset_rnn_on_terminal=True), critic_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.q_function.QFunction] = <nnabla_rl.algorithms.ddpg.DefaultCriticBuilder object>, critic_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.ddpg.DefaultSolverBuilder object>, actor_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.policy.DeterministicPolicy] = <nnabla_rl.algorithms.ddpg.DefaultActorBuilder object>, actor_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.ddpg.DefaultSolverBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.ddpg.DefaultReplayBufferBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.ddpg.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
Deep Deterministic Policy Gradient (DDPG) algorithm.
This class implements the modified version of the Deep Deterministic Policy Gradient (DDPG) algorithm proposed by T. P. Lillicrap, et al. in the paper: “Continuous control with deep reinforcement learning” For details see: https://arxiv.org/abs/1509.02971 We use gaussian noise instead of Ornstein-Uhlenbeck process to explore in the environment. The effectiveness of using gaussian noise for DDPG is reported in the paper: “Addressing Funciton Approximaiton Error in Actor-Critic Methods”. see https://arxiv.org/abs/1802.09477
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
DDPGConfig
) – configuration of the DDPG algorithmcritic_builder (
ModelBuilder[QFunction]
) – builder of critic modelscritic_solver_builder (
SolverBuilder
) – builder of critic solversactor_builder (
ModelBuilder[DeterministicPolicy]
) – builder of actor modelsactor_solver_builder (
SolverBuilder
) – builder of actor solversreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_bufferexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_rnn_supported()[source]¶
Check whether the algorithm supports rnn models or not
- Returns
True if the algorithm supports rnn models. Otherwise False.
- Return type
bool
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
DDQN¶
- class nnabla_rl.algorithms.ddqn.DDQNConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 0.00025, batch_size: int = 32, num_steps: int = 1, learner_update_frequency: float = 4, target_update_frequency: float = 10000, start_timesteps: int = 50000, replay_buffer_size: int = 1000000, max_explore_steps: int = 1000000, initial_epsilon: float = 1.0, final_epsilon: float = 0.1, test_epsilon: float = 0.05, grad_clip: Optional[Tuple[float, float]] = (- 1.0, 1.0), unroll_steps: int = 1, burn_in_steps: int = 0, reset_rnn_on_terminal: bool = True)[source]¶
Bases:
nnabla_rl.algorithms.dqn.DQNConfig
List of configurations for Double DQN (DDQN) algorithm
- Parameters
gamma (float) – discount factor of rewards. Defaults to 0.99.
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.00025.batch_size (int) – training batch size. Defaults to 32.
num_steps (int) – number of steps for N-step Q targets. Defaults to 1.
learner_update_frequency (int) – the interval of learner update. Defaults to 4.
target_update_frequency (int) – the interval of target q-function update. Defaults to 10000.
start_timesteps (int) – the timestep when training starts. The algorithm will collect experiences from the environment by acting randomly until this timestep. Defaults to 50000.
replay_buffer_size (int) – the capacity of replay buffer. Defaults to 1000000.
max_explore_steps (int) – the number of steps decaying the epsilon value. The epsilon will be decayed linearly \(\epsilon=\epsilon_{init} - step\times\frac{\epsilon_{init} - \epsilon_{final}}{max\_explore\_steps}\). Defaults to 1000000.
initial_epsilon (float) – the initial epsilon value for ε-greedy explorer. Defaults to 1.0.
final_epsilon (float) – the last epsilon value for ε-greedy explorer. Defaults to 0.1.
test_epsilon (float) – the epsilon value on testing. Defaults to 0.05.
grad_clip (Optional[Tuple[float, float]]) – Clip the gradient of final layer. Defaults to (-1.0, 1.0).
- class nnabla_rl.algorithms.ddqn.DDQN(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.ddqn.DDQNConfig = DDQNConfig(gpu_id=-1, gamma=0.99, learning_rate=0.00025, batch_size=32, num_steps=1, learner_update_frequency=4, target_update_frequency=10000, start_timesteps=50000, replay_buffer_size=1000000, max_explore_steps=1000000, initial_epsilon=1.0, final_epsilon=0.1, test_epsilon=0.05, grad_clip=(-1.0, 1.0), unroll_steps=1, burn_in_steps=0, reset_rnn_on_terminal=True), q_func_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.q_function.QFunction] = <nnabla_rl.algorithms.dqn.DefaultQFunctionBuilder object>, q_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.dqn.DefaultSolverBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.dqn.DefaultReplayBufferBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.dqn.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithms.dqn.DQN
Double DQN algorithm.
This class implements the Deep Q-Network with double q-learning (DDQN) algorithm proposed by H. van Hasselt, et al. in the paper: “Deep Reinforcement Learning with Double Q-learning” For details see: https://arxiv.org/abs/1509.06461
Note that default solver used in this implementation is RMSPropGraves as in the original paper. However, in practical applications, we recommend using Adam as the optimizer of DDQN. You can replace the solver by implementing a (
SolverBuilder
) and pass the solver on DDQN class instantiation.- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
DDQNConfig
) – the parameter for DDQN trainingq_func_builder (
ModelBuilder
) – builder of q function modelq_solver_builder (
SolverBuilder
) – builder of q function solverreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_buffer
DQN¶
- class nnabla_rl.algorithms.dqn.DQNConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 0.00025, batch_size: int = 32, num_steps: int = 1, learner_update_frequency: float = 4, target_update_frequency: float = 10000, start_timesteps: int = 50000, replay_buffer_size: int = 1000000, max_explore_steps: int = 1000000, initial_epsilon: float = 1.0, final_epsilon: float = 0.1, test_epsilon: float = 0.05, grad_clip: Optional[Tuple[float, float]] = (- 1.0, 1.0), unroll_steps: int = 1, burn_in_steps: int = 0, reset_rnn_on_terminal: bool = True)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for DQN algorithm
- Parameters
gamma (float) – discount factor of rewards. Defaults to 0.99.
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.00025.batch_size (int) – training batch size. Defaults to 32.
num_steps (int) – number of steps for N-step Q targets. Defaults to 1.
learner_update_frequency (int) – the interval of learner update. Defaults to 4.
target_update_frequency (int) – the interval of target q-function update. Defaults to 10000.
start_timesteps (int) – the timestep when training starts. The algorithm will collect experiences from the environment by acting randomly until this timestep. Defaults to 50000.
replay_buffer_size (int) – the capacity of replay buffer. Defaults to 1000000.
max_explore_steps (int) – the number of steps decaying the epsilon value. The epsilon will be decayed linearly \(\epsilon=\epsilon_{init} - step\times\frac{\epsilon_{init} - \epsilon_{final}}{max\_explore\_steps}\). Defaults to 1000000.
initial_epsilon (float) – the initial epsilon value for ε-greedy explorer. Defaults to 1.0.
final_epsilon (float) – the last epsilon value for ε-greedy explorer. Defaults to 0.1.
test_epsilon (float) – the epsilon value on testing. Defaults to 0.05.
grad_clip (Optional[Tuple[float, float]]) – Clip the gradient of final layer. Defaults to (-1.0, 1.0).
unroll_steps (int) – Number of steps to unroll tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
burn_in_steps (int) – Number of burn-in steps to initiaze recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
reset_rnn_on_terminal (bool) – Reset recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to False.
- class nnabla_rl.algorithms.dqn.DQN(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.dqn.DQNConfig = DQNConfig(gpu_id=-1, gamma=0.99, learning_rate=0.00025, batch_size=32, num_steps=1, learner_update_frequency=4, target_update_frequency=10000, start_timesteps=50000, replay_buffer_size=1000000, max_explore_steps=1000000, initial_epsilon=1.0, final_epsilon=0.1, test_epsilon=0.05, grad_clip=(-1.0, 1.0), unroll_steps=1, burn_in_steps=0, reset_rnn_on_terminal=True), q_func_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.q_function.QFunction] = <nnabla_rl.algorithms.dqn.DefaultQFunctionBuilder object>, q_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.dqn.DefaultSolverBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.dqn.DefaultReplayBufferBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.dqn.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
DQN algorithm.
This class implements the Deep Q-Network (DQN) algorithm proposed by V. Mnih, et al. in the paper: “Human-level control through deep reinforcement learning” For details see: https://www.nature.com/articles/nature14236
Note that default solver used in this implementation is RMSPropGraves as in the original paper. However, in practical applications, we recommend using Adam as the optimizer of DQN. You can replace the solver by implementing a (
SolverBuilder
) and pass the solver on DQN class instantiation.- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
DQNConfig
) – the parameter for DQN trainingq_func_builder (
ModelBuilder
) – builder of q function modelq_solver_builder (
SolverBuilder
) – builder of q function solverreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_bufferexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_rnn_supported()[source]¶
Check whether the algorithm supports rnn models or not
- Returns
True if the algorithm supports rnn models. Otherwise False.
- Return type
bool
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
DRQN¶
- class nnabla_rl.algorithms.drqn.DRQNConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 0.1, batch_size: int = 32, num_steps: int = 1, learner_update_frequency: float = 4, target_update_frequency: float = 10000, start_timesteps: int = 50000, replay_buffer_size: int = 400000, max_explore_steps: int = 1000000, initial_epsilon: float = 1.0, final_epsilon: float = 0.1, test_epsilon: float = 0.05, grad_clip: Optional[Tuple[float, float]] = (- 1.0, 1.0), unroll_steps: int = 10, burn_in_steps: int = 0, reset_rnn_on_terminal: bool = False, clip_grad_norm: float = 10.0)[source]¶
Bases:
nnabla_rl.algorithms.dqn.DQNConfig
List of configurations for DRQN algorithm. Most of the configs are inherited from DQNConfig
- Parameters
clip_grad_norm (float) – Limit the model parameter’s gradient on parameter updates up to this value. If you implement SolverBuilder by yourself, this value will not take effect. Defaults to 10.0.
learning_rate (float) – Solver learning rate. Value overridden from DQN. Defaults to 0.1.
replay_buffer_size (int) – Replay buffer size. Value overridden from DQN. Defaults to 400000.
unroll_steps (int) – Number of steps to unroll recurrent layer during training. Value overridden from DQN. Defaults to 10.
reset_rnn_on_terminal (bool) – Reset recurrent internal states to zero during training if episode ends. Value overridden from DQN. Defaults to False.
- class nnabla_rl.algorithms.drqn.DRQN(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.drqn.DRQNConfig = DRQNConfig(gpu_id=-1, gamma=0.99, learning_rate=0.1, batch_size=32, num_steps=1, learner_update_frequency=4, target_update_frequency=10000, start_timesteps=50000, replay_buffer_size=400000, max_explore_steps=1000000, initial_epsilon=1.0, final_epsilon=0.1, test_epsilon=0.05, grad_clip=(-1.0, 1.0), unroll_steps=10, burn_in_steps=0, reset_rnn_on_terminal=False, clip_grad_norm=10.0), q_func_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.q_function.QFunction] = <nnabla_rl.algorithms.drqn.DefaultQFunctionBuilder object>, q_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.drqn.DefaultSolverBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.dqn.DefaultReplayBufferBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.dqn.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithms.dqn.DQN
DRQN algorithm.
This class implements the Bootstrapped random update version of Deep Recurrent Q-Network (DRQN) algorithm. proposed by M. Hausknecht, et al. in the paper: “Deep Recurrent Q-Learning for Partially Observable MDPs” For details see: https://arxiv.org/pdf/1507.06527.pdf
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
DRQNConfig
) – the parameter for DRQN trainingq_func_builder (
ModelBuilder
) – builder of q function modelq_solver_builder (
SolverBuilder
) – builder of q function solverreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_bufferexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
GAIL¶
- class nnabla_rl.algorithms.gail.GAILConfig(gpu_id: int = - 1, preprocess_state: bool = True, act_deterministic_in_eval: bool = True, discriminator_batch_size: int = 50000, discriminator_learning_rate: float = 0.01, discriminator_update_frequency: int = 1, adversary_entropy_coef: float = 0.001, policy_update_frequency: int = 1, gamma: float = 0.995, lmb: float = 0.97, pi_batch_size: int = 50000, num_steps_per_iteration: int = 50000, sigma_kl_divergence_constraint: float = 0.01, maximum_backtrack_numbers: int = 10, conjugate_gradient_damping: float = 0.1, conjugate_gradient_iterations: int = 10, vf_epochs: int = 5, vf_batch_size: int = 128, vf_learning_rate: float = 0.001)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for GAIL algorithm
- Parameters
act_deterministic_in_eval (bool) – Enable act deterministically at evalution. Defaults to True.
discriminator_batch_size (bool) – Trainig batch size of discriminator. Usually, discriminator_batch_size is the same as pi_batch_size. Defaults to 50000.
discriminator_learning_rate (float) – Learning rate which is set to the solvers of dicriminator function. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.001.discriminator_update_frequency (int) – Frequency (measured in the number of parameter update) of discriminator update. Defaults to 1.
adversary_entropy_coef (float) – Coefficient of entropy loss in dicriminator training. Defaults to 0.001.
policy_update_frequency (int) – Frequency (measured in the number of parameter update) of policy update. Defaults to 1.
gamma (float) – Discount factor of rewards. Defaults to 0.995.
lmb (float) – Scalar of lambda return’s computation in GAE. Defaults to 0.97. This configuration is related to bias and variance of estimated value. If it is close to 0, estimated value is low-variance but biased. If it is close to 1, estimated value is unbiased but high-variance.
num_steps_per_iteration (int) – Number of steps per each training iteration for collecting on-policy experinces. Increasing this step size is effective to get precise parameters of policy and value function updating, but computational time of each iteration will increase. Defaults to 50000.
pi_batch_size (int) – Trainig batch size of policy. Usually, pi_batch_size is the same as num_steps_per_iteration. Defaults to 50000.
sigma_kl_divergence_constraint (float) – Constraint size of kl divergence between previous policy and updated policy. Defaults to 0.01.
maximum_backtrack_numbers (int) – Maximum backtrack numbers of linesearch. Defaults to 10.
conjugate_gradient_damping (float) – Damping size of conjugate gradient method. Defaults to 0.1.
conjugate_gradient_iterations (int) – Number of iterations of conjugate gradient method. Defaults to 10.
vf_epochs (int) – Number of epochs in each iteration. Defaults to 5.
vf_batch_size (int) – Training batch size of value function. Defaults to 128.
vf_learning_rate (float) – Learning rate which is set to the solvers of value function. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.001.preprocess_state (bool) – Enable preprocessing the states in the collected experiences before feeding as training batch. Defaults to True.
- class nnabla_rl.algorithms.gail.GAIL(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], expert_buffer: nnabla_rl.replay_buffer.ReplayBuffer, config: nnabla_rl.algorithms.gail.GAILConfig = GAILConfig(gpu_id=-1, preprocess_state=True, act_deterministic_in_eval=True, discriminator_batch_size=50000, discriminator_learning_rate=0.01, discriminator_update_frequency=1, adversary_entropy_coef=0.001, policy_update_frequency=1, gamma=0.995, lmb=0.97, pi_batch_size=50000, num_steps_per_iteration=50000, sigma_kl_divergence_constraint=0.01, maximum_backtrack_numbers=10, conjugate_gradient_damping=0.1, conjugate_gradient_iterations=10, vf_epochs=5, vf_batch_size=128, vf_learning_rate=0.001), v_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.v_function.VFunction] = <nnabla_rl.algorithms.gail.DefaultVFunctionBuilder object>, v_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.gail.DefaultVFunctionSolverBuilder object>, policy_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.policy.StochasticPolicy] = <nnabla_rl.algorithms.gail.DefaultPolicyBuilder object>, reward_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.reward_function.RewardFunction] = <nnabla_rl.algorithms.gail.DefaultRewardFunctionBuilder object>, reward_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.gail.DefaultRewardFunctionSolverBuilder object>, state_preprocessor_builder: typing.Optional[nnabla_rl.builders.preprocessor_builder.PreprocessorBuilder] = <nnabla_rl.algorithms.gail.DefaultPreprocessorBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.gail.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
Generative Adversarial Imitation Learning implementation.
This class implements the Generative Adversarial Imitation Learning (GAIL) algorithm proposed by Jonathan Ho, et al. in the paper: “Generative Adversarial Imitation Learning” For detail see: https://arxiv.org/abs/1606.03476
This algorithm only supports online training.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoexpert_buffer (
ReplayBuffer
) – replay buffer which contains expert experience.config (
GAILConfig
) – configuration of GAIL algorithmv_function_builder (
ModelBuilder[VFunction]
) – builder of v function modelsv_solver_builder (
SolverBuilder
) – builder for v function solverspolicy_builder (
ModelBuilder[StochasicPolicy]
) – builder of policy modelsreward_function_builder (
ModelBuilder[RewardFunction]
) – builder of reward function modelsreward_solver_builder (
SolverBuilder
) – builder for reward function solversstate_preprocessor_builder (None or
PreprocessorBuilder
) – state preprocessor builder to preprocess the statesexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
HER¶
- class nnabla_rl.algorithms.her.HERConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 0.001, batch_size: int = 100, tau: float = 0.005, start_timesteps: int = 10000, replay_buffer_size: int = 1000000, exploration_noise_sigma: float = 0.1, num_steps: int = 1, actor_unroll_steps: int = 1, actor_burn_in_steps: int = 0, actor_reset_rnn_on_terminal: bool = True, critic_unroll_steps: int = 1, critic_burn_in_steps: int = 0, critic_reset_rnn_on_terminal: bool = True, n_cycles: int = 50, n_rollout: int = 16, n_update: int = 40, max_timesteps: int = 50, hindsight_prob: float = 0.8, action_loss_coef: float = 1.0, return_clip: Optional[Tuple[float, float]] = (- 50.0, 0.0), exploration_epsilon: float = 0.3, preprocess_state: bool = True, normalize_epsilon: float = 0.01, normalize_clip_range: Optional[Tuple[float, float]] = (- 5.0, 5.0), observation_clip_range: Optional[Tuple[float, float]] = (- 200.0, 200.0))[source]¶
Bases:
nnabla_rl.algorithms.ddpg.DDPGConfig
List of configurations for HER algorithm
- Parameters
gamma (float) – discount factor of rewards. Defaults to 0.99.
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.001.batch_size (int) – training batch size. Defaults to 100.
tau (float) – target network’s parameter update coefficient. Defaults to 0.005.
start_timesteps (int) – the timestep when training starts. The algorithm will collect experiences from the environment by acting randomly until this timestep. Defaults to 10000.
replay_buffer_size (int) – capacity of the replay buffer. Defaults to 1000000.
exploration_noise_sigma (float) – standard deviation of gaussian exploration noise. Defaults to 0.1.
n_cycles (int) – the number of cycle. A cycle means collecting experiences for some episodes and updating model for several times.
n_rollout (int) – the number of episode in which policy collect experiences.
n_update (int) – the number of updating model
max_timesteps (int) – the timestep when finishing one epsode.
hindsight_prob (float) – the probability at which buffer samples hindsight goal.
action_loss_coef (float) – the value of coefficient about action loss in policy trainer.
return_clip (Optional[Tuple[float, float]]) – the range of clipping return value.
exploration_epsilon (float) – the value for ε-greedy explorer.
preprocess_state (bool) – Enable preprocessing the states in the collected experiences before feeding as training batch. Defaults to True.
normalize_epsilon (float) – the minimum value of standard deviation of preprocessed state.
normalize_clip_range (Optional[Tuple[float, float]]) – the range of clipping state.
observation_clip_range (Optional[Tuple[float, float]]) – the range of clipping observation.
- class nnabla_rl.algorithms.her.HER(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.her.HERConfig = HERConfig(gpu_id=-1, gamma=0.99, learning_rate=0.001, batch_size=100, tau=0.005, start_timesteps=10000, replay_buffer_size=1000000, exploration_noise_sigma=0.1, num_steps=1, actor_unroll_steps=1, actor_burn_in_steps=0, actor_reset_rnn_on_terminal=True, critic_unroll_steps=1, critic_burn_in_steps=0, critic_reset_rnn_on_terminal=True, n_cycles=50, n_rollout=16, n_update=40, max_timesteps=50, hindsight_prob=0.8, action_loss_coef=1.0, return_clip=(-50.0, 0.0), exploration_epsilon=0.3, preprocess_state=True, normalize_epsilon=0.01, normalize_clip_range=(-5.0, 5.0), observation_clip_range=(-200.0, 200.0)), critic_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.q_function.QFunction] = <nnabla_rl.algorithms.her.HERCriticBuilder object>, critic_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.her.HERSolverBuilder object>, actor_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.policy.DeterministicPolicy] = <nnabla_rl.algorithms.her.HERActorBuilder object>, actor_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.her.HERSolverBuilder object>, state_preprocessor_builder: typing.Optional[nnabla_rl.builders.preprocessor_builder.PreprocessorBuilder] = <nnabla_rl.algorithms.her.HERPreprocessorBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.her.HindsightReplayBufferBuilder object>)[source]¶
Bases:
nnabla_rl.algorithms.ddpg.DDPG
Hindsight Experience Replay (HER) algorithm implementation.
This class implements the Hindsight Experience Replay (HER) algorithm proposed by M. Andrychowicz, et al. in the paper: “Hindsight Experience Replay” For detail see: https://arxiv.org/abs/1707.06347
This algorithm only supports online training.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
HERConfig
) – configuration of HER algorithmcritic_builder (
ModelBuilder[VFunction]
) – builder of critic modelscritic_solver_builder (
SolverBuilder
) – builder for critic solversactor_builder (
ModelBuilder[StochasicPolicy]
) – builder of actor modelsactor_solver_builder (
SolverBuilder
) – builder for actor solversstate_preprocessor_builder (None or
PreprocessorBuilder
) – state preprocessor builder to preprocess the statesreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_buffer
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
IQN¶
- class nnabla_rl.algorithms.iqn.IQNConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 5e-05, batch_size: int = 32, num_steps: int = 1, start_timesteps: int = 50000, replay_buffer_size: int = 1000000, learner_update_frequency: int = 4, target_update_frequency: int = 10000, max_explore_steps: int = 1000000, initial_epsilon: float = 1.0, final_epsilon: float = 0.01, test_epsilon: float = 0.001, N: int = 64, N_prime: int = 64, K: int = 32, kappa: float = 1.0, embedding_dim: int = 64, unroll_steps: int = 1, burn_in_steps: int = 0, reset_rnn_on_terminal: bool = True)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for IQN algorithm
- Parameters
gamma (float) – discount factor of rewards. Defaults to 0.99.
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.00005.batch_size (int) – training batch size. Defaults to 32.
num_steps (int) – number of steps for N-step Q targets. Defaults to 1.
start_timesteps (int) – the timestep when training starts. The algorithm will collect experiences from the environment by acting randomly until this timestep. Defaults to 50000.
replay_buffer_size (int) – the capacity of replay buffer. Defaults to 1000000.
learner_update_frequency (int) – the interval of learner update. Defaults to 4.
target_update_frequency (int) – the interval of target q-function update. Defaults to 10000.
max_explore_steps (int) – the number of steps decaying the epsilon value. The epsilon will be decayed linearly \(\epsilon=\epsilon_{init} - step\times\frac{\epsilon_{init} - \epsilon_{final}}{max\_explore\_steps}\). Defaults to 1000000.
initial_epsilon (float) – the initial epsilon value for ε-greedy explorer. Defaults to 1.0.
final_epsilon (float) – the last epsilon value for ε-greedy explorer. Defaults to 0.01.
test_epsilon (float) – the epsilon value on testing. Defaults to 0.001.
N (int) – Number of samples to compute the current state’s quantile values. Defaults to 64.
N_prime (int) – Number of samples to compute the target state’s quantile values. Defaults to 64.
K (int) – Number of samples to compute greedy next action. Defaults to 32.
kappa (float) – threshold value of quantile huber loss. Defaults to 1.0.
embedding_dim (int) – dimension of embedding for the sample point. Defaults to 64.
unroll_steps (int) – Number of steps to unroll tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
burn_in_steps (int) – Number of burn-in steps to initiaze recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
reset_rnn_on_terminal (bool) – Reset recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to True.
- class nnabla_rl.algorithms.iqn.IQN(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.iqn.IQNConfig = IQNConfig(gpu_id=-1, gamma=0.99, learning_rate=5e-05, batch_size=32, num_steps=1, start_timesteps=50000, replay_buffer_size=1000000, learner_update_frequency=4, target_update_frequency=10000, max_explore_steps=1000000, initial_epsilon=1.0, final_epsilon=0.01, test_epsilon=0.001, N=64, N_prime=64, K=32, kappa=1.0, embedding_dim=64, unroll_steps=1, burn_in_steps=0, reset_rnn_on_terminal=True), quantile_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.distributional_function.StateActionQuantileFunction] = <nnabla_rl.algorithms.iqn.DefaultQuantileFunctionBuilder object>, quantile_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.iqn.DefaultSolverBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.iqn.DefaultReplayBufferBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.iqn.DefaultExplorerBuilder object>, risk_measure_function=<function risk_neutral_measure>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
Implicit Quantile Network algorithm.
This class implements the Implicit Quantile Network (IQN) algorithm proposed by W. Dabney, et al. in the paper: “Implicit Quantile Networks for Distributional Reinforcement Learning” For details see: https://arxiv.org/pdf/1806.06923.pdf
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
IQNConfig
) – configuration of IQN algorithmquantile_function_builder (
ModelBuilder[StateActionQuantileFunction]
) – buider of state-action quantile function modelsquantile_solver_builder (
SolverBuilder
) – builder for state action quantile function solversreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_bufferexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_rnn_supported()[source]¶
Check whether the algorithm supports rnn models or not
- Returns
True if the algorithm supports rnn models. Otherwise False.
- Return type
bool
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
MMESAC¶
- class nnabla_rl.algorithms.mme_sac.MMESACConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 0.00030000000000000003, batch_size: int = 256, tau: float = 0.005, environment_steps: int = 1, gradient_steps: int = 1, reward_scalar: float = 5.0, start_timesteps: int = 10000, replay_buffer_size: int = 1000000, target_update_interval: int = 1, num_steps: int = 1, pi_unroll_steps: int = 1, pi_burn_in_steps: int = 0, pi_reset_rnn_on_terminal: bool = True, q_unroll_steps: int = 1, q_burn_in_steps: int = 0, q_reset_rnn_on_terminal: bool = True, v_unroll_steps: int = 1, v_burn_in_steps: int = 0, v_reset_rnn_on_terminal: bool = True, alpha_pi: Optional[float] = None, alpha_q: float = 1.0)[source]¶
Bases:
nnabla_rl.algorithms.icml2018_sac.ICML2018SACConfig
List of configurations for MMESAC algorithm.
- Parameters
alpha_pi (Optional[float]) – If None, will use reward_scalar to scale the reward. Otherwise 1/alpha_pi will be used to scale the reward. Defaults to None.
alpha_q (float) – Temperature value for negative entropy term. Defaults to 1.0.
- class nnabla_rl.algorithms.mme_sac.MMESAC(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.mme_sac.MMESACConfig = MMESACConfig(gpu_id=-1, gamma=0.99, learning_rate=0.00030000000000000003, batch_size=256, tau=0.005, environment_steps=1, gradient_steps=1, reward_scalar=5.0, start_timesteps=10000, replay_buffer_size=1000000, target_update_interval=1, num_steps=1, pi_unroll_steps=1, pi_burn_in_steps=0, pi_reset_rnn_on_terminal=True, q_unroll_steps=1, q_burn_in_steps=0, q_reset_rnn_on_terminal=True, v_unroll_steps=1, v_burn_in_steps=0, v_reset_rnn_on_terminal=True, alpha_pi=None, alpha_q=1.0), v_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.v_function.VFunction] = <nnabla_rl.algorithms.icml2018_sac.DefaultVFunctionBuilder object>, v_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.icml2018_sac.DefaultSolverBuilder object>, q_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.q_function.QFunction] = <nnabla_rl.algorithms.icml2018_sac.DefaultQFunctionBuilder object>, q_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.icml2018_sac.DefaultSolverBuilder object>, policy_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.policy.StochasticPolicy] = <nnabla_rl.algorithms.icml2018_sac.DefaultPolicyBuilder object>, policy_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.icml2018_sac.DefaultSolverBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.icml2018_sac.DefaultReplayBufferBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.icml2018_sac.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithms.icml2018_sac.ICML2018SAC
Max-Min Entropy Soft Actor-Critic (MME-SAC) algorithm.
This class implements the Max-Min Entropy Soft Actor Critic (MME-SAC) algorithm proposed by S. Han, et al. in the paper: “A Max-Min Entropy Framework for Reinforcement Learning” For details see: https://arxiv.org/abs/2106.10517
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
MMESACConfig
) – configuration of the MMESAC algorithmv_function_builder (
ModelBuilder[VFunction]
) – builder of v function modelsv_solver_builder (
SolverBuilder
) – builder of v function solversq_function_builder (
ModelBuilder[QFunction]
) – builder of q function modelsq_solver_builder (
SolverBuilder
) – builder of q function solverspolicy_builder (
ModelBuilder[StochasticPolicy]
) – builder of actor modelspolicy_solver_builder (
SolverBuilder
) – builder of policy solversreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_bufferexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
MMESAC (Disentangled)¶
- class nnabla_rl.algorithms.demme_sac.DEMMESACConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 0.00030000000000000003, batch_size: int = 256, tau: float = 0.005, environment_steps: int = 1, gradient_steps: int = 1, start_timesteps: int = 10000, replay_buffer_size: int = 1000000, target_update_interval: int = 1, num_rr_steps: int = 1, num_re_steps: int = 1, reward_scalar: float = 5.0, alpha_pi: Optional[float] = None, alpha_q: float = 1.0, pi_t_unroll_steps: int = 1, pi_t_burn_in_steps: int = 0, pi_t_reset_rnn_on_terminal: bool = True, pi_e_unroll_steps: int = 1, pi_e_burn_in_steps: int = 0, pi_e_reset_rnn_on_terminal: bool = True, q_rr_unroll_steps: int = 1, q_rr_burn_in_steps: int = 0, q_rr_reset_rnn_on_terminal: bool = True, q_re_unroll_steps: int = 1, q_re_burn_in_steps: int = 0, q_re_reset_rnn_on_terminal: bool = True, v_rr_unroll_steps: int = 1, v_rr_burn_in_steps: int = 0, v_rr_reset_rnn_on_terminal: bool = True, v_re_unroll_steps: int = 1, v_re_burn_in_steps: int = 0, v_re_reset_rnn_on_terminal: bool = True)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for DEMMESAC algorithm.
- Parameters
gamma (float) – discount factor of rewards. Defaults to 0.99.
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.0003.batch_size (int) – training batch size. Defaults to 256.
tau (float) – target network’s parameter update coefficient. Defaults to 0.005.
environment_steps (int) – Number of steps to interact with the environment on each iteration. Defaults to 1.
gradient_steps (int) – Number of parameter updates to perform on each iteration. Defaults to 1.
reward_scalar (float) – Reward scaling factor. Obtained reward will be multiplied by this value. Defaults to 5.0.
start_timesteps (int) – the timestep when training starts. The algorithm will collect experiences from the environment by acting randomly until this timestep. Defaults to 10000.
replay_buffer_size (int) – capacity of the replay buffer. Defaults to 1000000.
num_rr_steps (int) – number of steps for N-step Q_rr targets. Defaults to 1.
num_re_steps (int) – number of steps for N-step Q_re targets. Defaults to 1.
target_update_interval (float) – the interval of target v function parameter’s update. Defaults to 1.
pi_t_unroll_steps (int) – Number of steps to unroll policy’s (pi_t) tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
pi_e_unroll_steps (int) – Number of steps to unroll policy’s (pi_e) tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
pi_t_burn_in_steps (int) – Number of burn-in steps to initiaze policy’s (pi_t) recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
pi_e_burn_in_steps (int) – Number of burn-in steps to initiaze policy’s (pi_e) recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
pi_t_reset_rnn_on_terminal (bool) – Reset policy’s (pi_t) recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to False.
pi_e_reset_rnn_on_terminal (bool) – Reset policy’s (pi_e) recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to False.
q_rr_unroll_steps (int) – Number of steps to unroll q-function’s (q_rr) tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
q_re_unroll_steps (int) – Number of steps to unroll q-function’s (q_re) tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
q_rr_burn_in_steps (int) – Number of burn-in steps to initiaze q-function’s (q_rr) recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
q_re_burn_in_steps (int) – Number of burn-in steps to initiaze q-function’s (q_re) recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
q_rr_reset_rnn_on_terminal (bool) – Reset q-function’s (q_rr) recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to False.
q_re_reset_rnn_on_terminal (bool) – Reset q-function’s (q_re) recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to False.
v_rr_unroll_steps (int) – Number of steps to unroll v-function’s (v_rr) tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
v_re_unroll_steps (int) – Number of steps to unroll v-function’s (v_re) tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
v_rr_burn_in_steps (int) – Number of burn-in steps to initiaze v-function’s (v_rr) recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
v_re_burn_in_steps (int) – Number of burn-in steps to initiaze v-function’s (v_re) recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
v_rr_reset_rnn_on_terminal (bool) – Reset v-function’s (v_rr) recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to False.
v_re_reset_rnn_on_terminal (bool) – Reset v-function’s (v_re) recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to False.
alpha_pi (Optional[float]) – If None, will use reward_scalar to scale the reward. Otherwise 1/alpha_pi will be used to scale the reward. Defaults to None.
alpha_q (float) – Temperature value for negative entropy term. Defaults to 1.0.
- class nnabla_rl.algorithms.demme_sac.DEMMESAC(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.demme_sac.DEMMESACConfig = DEMMESACConfig(gpu_id=-1, gamma=0.99, learning_rate=0.00030000000000000003, batch_size=256, tau=0.005, environment_steps=1, gradient_steps=1, start_timesteps=10000, replay_buffer_size=1000000, target_update_interval=1, num_rr_steps=1, num_re_steps=1, reward_scalar=5.0, alpha_pi=None, alpha_q=1.0, pi_t_unroll_steps=1, pi_t_burn_in_steps=0, pi_t_reset_rnn_on_terminal=True, pi_e_unroll_steps=1, pi_e_burn_in_steps=0, pi_e_reset_rnn_on_terminal=True, q_rr_unroll_steps=1, q_rr_burn_in_steps=0, q_rr_reset_rnn_on_terminal=True, q_re_unroll_steps=1, q_re_burn_in_steps=0, q_re_reset_rnn_on_terminal=True, v_rr_unroll_steps=1, v_rr_burn_in_steps=0, v_rr_reset_rnn_on_terminal=True, v_re_unroll_steps=1, v_re_burn_in_steps=0, v_re_reset_rnn_on_terminal=True), v_rr_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.v_function.VFunction] = <nnabla_rl.algorithms.demme_sac.DefaultVFunctionBuilder object>, v_rr_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.demme_sac.DefaultSolverBuilder object>, v_re_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.v_function.VFunction] = <nnabla_rl.algorithms.demme_sac.DefaultVFunctionBuilder object>, v_re_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.demme_sac.DefaultSolverBuilder object>, q_rr_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.q_function.QFunction] = <nnabla_rl.algorithms.demme_sac.DefaultQFunctionBuilder object>, q_rr_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.demme_sac.DefaultSolverBuilder object>, q_re_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.q_function.QFunction] = <nnabla_rl.algorithms.demme_sac.DefaultQFunctionBuilder object>, q_re_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.demme_sac.DefaultSolverBuilder object>, pi_t_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.policy.StochasticPolicy] = <nnabla_rl.algorithms.demme_sac.DefaultPolicyBuilder object>, pi_t_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.demme_sac.DefaultSolverBuilder object>, pi_e_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.policy.StochasticPolicy] = <nnabla_rl.algorithms.demme_sac.DefaultPolicyBuilder object>, pi_e_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.demme_sac.DefaultSolverBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.demme_sac.DefaultReplayBufferBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.demme_sac.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
DisEntangled Max-Min Entropy Soft Actor-Critic (DEMME-SAC) algorithm.
This class implements the disentangled version of max-min Soft Actor Critic (SAC) algorithm proposed by S. Han, et al. in the paper: “A Max-Min Entropy Framework for Reinforcement Learning” For detail see: https://arxiv.org/abs/2106.10517
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
DEMMESACConfig
) – configuration of the DEMMESAC algorithmv_rr_function_builder (
ModelBuilder[VFunction]
) – builder of reward v function modelsv_rr_solver_builder (
SolverBuilder
) – builder of reward v function solversv_re_function_builder (
ModelBuilder[VFunction]
) – builder of entropy v function modelsv_re_solver_builder (
SolverBuilder
) – builder of entropyv function solversq_rr_function_builder (
ModelBuilder[QFunction]
) – builder of reward q function modelsq_rr_solver_builder (
SolverBuilder
) – builder of reward q function solversq_re_function_builder (
ModelBuilder[QFunction]
) – builder of entropy q function modelsq_re_solver_builder (
SolverBuilder
) – builder of entropy q function solverspi_t_builder (
ModelBuilder[StochasticPolicy]
) – builder of target policy modelspi_t_solver_builder (
SolverBuilder
) – builder of target policy solverspi_e_builder (
ModelBuilder[StochasticPolicy]
) – builder of pure exploration policy modelspi_e_solver_builder (
SolverBuilder
) – builder of pure exploration policy solversreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_bufferexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_rnn_supported()[source]¶
Check whether the algorithm supports rnn models or not
- Returns
True if the algorithm supports rnn models. Otherwise False.
- Return type
bool
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
Munchausen DQN¶
- class nnabla_rl.algorithms.munchausen_dqn.MunchausenDQNConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 5e-05, batch_size: int = 32, num_steps: int = 1, learner_update_frequency: float = 4, target_update_frequency: float = 10000, start_timesteps: int = 50000, replay_buffer_size: int = 1000000, max_explore_steps: int = 1000000, initial_epsilon: float = 1.0, final_epsilon: float = 0.01, test_epsilon: float = 0.001, grad_clip: Optional[Tuple[float, float]] = (- 1.0, 1.0), unroll_steps: int = 1, burn_in_steps: int = 0, reset_rnn_on_terminal: bool = True, entropy_temperature: float = 0.03, munchausen_scaling_term: float = 0.9, clipping_value: float = - 1)[source]¶
Bases:
nnabla_rl.algorithms.dqn.DQNConfig
List of configurations for Munchausen DQN algorithm
- Parameters
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.00005.final_epsilon (float) – the last epsilon value for ε-greedy explorer. Defaults to 0.01.
test_epsilon (float) – the epsilon value on testing. Defaults to 0.001.
entropy_temperature (float) – temperature parameter of softmax policy distribution. Defaults to 0.03.
munchausen_scaling_term (float) – scalar of scaled log policy. Defaults to 0.9.
clipping_value (float) – Lower value of the logarithm of policy distribution. Defaults to -1.
- class nnabla_rl.algorithms.munchausen_dqn.MunchausenDQN(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.munchausen_dqn.MunchausenDQNConfig = MunchausenDQNConfig(gpu_id=-1, gamma=0.99, learning_rate=5e-05, batch_size=32, num_steps=1, learner_update_frequency=4, target_update_frequency=10000, start_timesteps=50000, replay_buffer_size=1000000, max_explore_steps=1000000, initial_epsilon=1.0, final_epsilon=0.01, test_epsilon=0.001, grad_clip=(-1.0, 1.0), unroll_steps=1, burn_in_steps=0, reset_rnn_on_terminal=True, entropy_temperature=0.03, munchausen_scaling_term=0.9, clipping_value=-1), q_func_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.q_function.QFunction] = <nnabla_rl.algorithms.dqn.DefaultQFunctionBuilder object>, q_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.munchausen_dqn.DefaultQSolverBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.dqn.DefaultReplayBufferBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.dqn.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithms.dqn.DQN
Munchausen-DQN algorithm.
This class implements the Munchausen-DQN (Munchausen Deep Q Network) algorithm proposed by N. Vieillard, et al. in the paper: “Munchausen Reinforcement Learning” For details see: https://proceedings.neurips.cc/paper/2020/file/2c6a0bae0f071cbbf0bb3d5b11d90a82-Paper.pdf
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
MunchausenDQNConfig
) – configuration of MunchausenDQN algorithmq_func_builder (
ModelBuilder[QFunction]
) – builder of q-function modelsq_solver_builder (
SolverBuilder
) – builder for q-function solversreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_bufferexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
Munchausen IQN¶
- class nnabla_rl.algorithms.munchausen_iqn.MunchausenIQNConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 5e-05, batch_size: int = 32, num_steps: int = 1, start_timesteps: int = 50000, replay_buffer_size: int = 1000000, learner_update_frequency: int = 4, target_update_frequency: int = 10000, max_explore_steps: int = 1000000, initial_epsilon: float = 1.0, final_epsilon: float = 0.01, test_epsilon: float = 0.001, N: int = 64, N_prime: int = 64, K: int = 32, kappa: float = 1.0, embedding_dim: int = 64, unroll_steps: int = 1, burn_in_steps: int = 0, reset_rnn_on_terminal: bool = True, entropy_temperature: float = 0.03, munchausen_scaling_term: float = 0.9, clipping_value: float = - 1)[source]¶
Bases:
nnabla_rl.algorithms.iqn.IQNConfig
List of configurations for Munchausen IQN algorithm
- Parameters
entropy_temperature (float) – temperature parameter of softmax policy distribution. Defaults to 0.03.
munchausen_scaling_term (float) – scalar of scaled log policy. Defaults to 0.9.
clipping_value (float) – Lower value of the logarithm of policy distribution. Defaults to -1.
- class nnabla_rl.algorithms.munchausen_iqn.MunchausenIQN(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.munchausen_iqn.MunchausenIQNConfig = MunchausenIQNConfig(gpu_id=-1, gamma=0.99, learning_rate=5e-05, batch_size=32, num_steps=1, start_timesteps=50000, replay_buffer_size=1000000, learner_update_frequency=4, target_update_frequency=10000, max_explore_steps=1000000, initial_epsilon=1.0, final_epsilon=0.01, test_epsilon=0.001, N=64, N_prime=64, K=32, kappa=1.0, embedding_dim=64, unroll_steps=1, burn_in_steps=0, reset_rnn_on_terminal=True, entropy_temperature=0.03, munchausen_scaling_term=0.9, clipping_value=-1), risk_measure_function=<function risk_neutral_measure>, quantile_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.distributional_function.StateActionQuantileFunction] = <nnabla_rl.algorithms.iqn.DefaultQuantileFunctionBuilder object>, quantile_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.iqn.DefaultSolverBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.iqn.DefaultReplayBufferBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.iqn.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithms.iqn.IQN
Munchausen-IQN algorithm implementation.
This class implements the Munchausen-IQN (Munchausen Implicit Quantile Network) algorithm proposed by N. Vieillard, et al. in the paper: “Munchausen Reinforcement Learning” For details see: https://proceedings.neurips.cc/paper/2020/file/2c6a0bae0f071cbbf0bb3d5b11d90a82-Paper.pdf
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
MunchausenIQNConfig
) – configuration of MunchausenIQN algorithmrisk_measure_function (Callable[[nn.Variable], nn.Variable]) – risk measure function to apply to the quantiles.
quantile_function_builder (
ModelBuilder[StateActionQuantileFunction]
) – builder of state-action quantile function modelsquantile_solver_builder (
SolverBuilder
) – builder for state action quantile function solversreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_bufferexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
PPO¶
- class nnabla_rl.algorithms.ppo.PPOConfig(gpu_id: int = - 1, epsilon: float = 0.1, gamma: float = 0.99, learning_rate: float = 0.00025, lmb: float = 0.95, entropy_coefficient: float = 0.01, value_coefficient: float = 1.0, actor_num: int = 8, epochs: int = 3, batch_size: int = 256, actor_timesteps: int = 128, total_timesteps: int = 10000, decrease_alpha: bool = True, timelimit_as_terminal: bool = False, seed: int = 1, preprocess_state: bool = True)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for PPO algorithm
- Parameters
epsilon (float) – PPO’s probability ratio clipping range. Defaults to 0.1
gamma (float) – discount factor of rewards. Defaults to 0.99.
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.00025.batch_size (int) – training batch size. Defaults to 256.
lmb (float) – scalar of lambda return’s computation in GAE. Defaults to 0.95.
entropy_coefficient (float) – scalar of entropy regularization term. Defaults to 0.01.
value_coefficient (float) – scalar of value loss. Defaults to 1.0.
actor_num (int) – Number of parallel actors. Defaults to 8.
epochs (int) – Number of epochs to perform in each training iteration. Defaults to 3.
actor_timesteps (int) – Number of timesteps to interact with the environment by the actors. Defaults to 128.
total_timesteps (int) – Total number of timesteps to interact with the environment. Defaults to 10000.
decrease_alpha (bool) – Flag to control whether to decrease the learning rate linearly during the training. Defaults to True.
timelimit_as_terminal (bool) –
Treat as done if the environment reaches the timelimit. Defaults to False.
seed (int) – base seed of random number generator used by the actors. Defaults to 1.
preprocess_state (bool) – Enable preprocessing the states in the collected experiences before feeding as training batch. Defaults to True.
- class nnabla_rl.algorithms.ppo.PPO(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.ppo.PPOConfig = PPOConfig(gpu_id=-1, epsilon=0.1, gamma=0.99, learning_rate=0.00025, lmb=0.95, entropy_coefficient=0.01, value_coefficient=1.0, actor_num=8, epochs=3, batch_size=256, actor_timesteps=128, total_timesteps=10000, decrease_alpha=True, timelimit_as_terminal=False, seed=1, preprocess_state=True), v_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.v_function.VFunction] = <nnabla_rl.algorithms.ppo.DefaultVFunctionBuilder object>, v_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.ppo.DefaultSolverBuilder object>, policy_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.policy.StochasticPolicy] = <nnabla_rl.algorithms.ppo.DefaultPolicyBuilder object>, policy_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.ppo.DefaultSolverBuilder object>, state_preprocessor_builder: typing.Optional[nnabla_rl.builders.preprocessor_builder.PreprocessorBuilder] = <nnabla_rl.algorithms.ppo.DefaultPreprocessorBuilder object>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
Proximal Policy Optimization (PPO) algorithm implementation.
This class implements the Proximal Policy Optimization (PPO) algorithm proposed by J. Schulman, et al. in the paper: “Proximal Policy Optimization Algorithms” For detail see: https://arxiv.org/abs/1707.06347
This algorithm only supports online training.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
PPOConfig
) – configuration of PPO algorithmv_function_builder (
ModelBuilder[VFunction]
) – builder of v function modelsv_solver_builder (
SolverBuilder
) – builder for v function solverspolicy_builder (
ModelBuilder[StochasicPolicy]
) – builder of policy modelspolicy_solver_builder (
SolverBuilder
) – builder for policy solversstate_preprocessor_builder (None or
PreprocessorBuilder
) – state preprocessor builder to preprocess the states
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
QRDQN¶
- class nnabla_rl.algorithms.qrdqn.QRDQNConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 5e-05, batch_size: int = 32, num_steps: int = 1, learner_update_frequency: int = 4, target_update_frequency: int = 10000, start_timesteps: int = 50000, replay_buffer_size: int = 1000000, max_explore_steps: int = 1000000, initial_epsilon: float = 1.0, final_epsilon: float = 0.01, test_epsilon: float = 0.001, num_quantiles: int = 200, kappa: float = 1.0, unroll_steps: int = 1, burn_in_steps: int = 0, reset_rnn_on_terminal: bool = True)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for QRDQN algorithm
- Parameters
gamma (float) – discount factor of rewards. Defaults to 0.99.
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.00005.batch_size (int) – training batch size. Defaults to 32.
num_steps (int) – number of steps for N-step Q targets. Defaults to 1.
learner_update_frequency (int) – the interval of learner update. Defaults to 4.
target_update_frequency (int) – the interval of target q-function update. Defaults to 10000.
start_timesteps (int) – the timestep when training starts. The algorithm will collect experiences from the environment by acting randomly until this timestep. Defaults to 50000.
replay_buffer_size (int) – the capacity of replay buffer. Defaults to 1000000.
max_explore_steps (int) – the number of steps decaying the epsilon value. The epsilon will be decayed linearly \(\epsilon=\epsilon_{init} - step\times\frac{\epsilon_{init} - \epsilon_{final}}{max\_explore\_steps}\). Defaults to 1000000.
initial_epsilon (float) – the initial epsilon value for ε-greedy explorer. Defaults to 1.0.
final_epsilon (float) – the last epsilon value for ε-greedy explorer. Defaults to 0.01.
test_epsilon (float) – the epsilon value on testing. Defaults to 0.001.
num_quantiles (int) – Number of quantile points. Defaults to 200.
kappa (float) – threshold value of quantile huber loss. Defaults to 1.0.
unroll_steps (int) – Number of steps to unroll tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
burn_in_steps (int) – Number of burn-in steps to initiaze recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
reset_rnn_on_terminal (bool) – Reset recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to True.
- class nnabla_rl.algorithms.qrdqn.QRDQN(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.qrdqn.QRDQNConfig = QRDQNConfig(gpu_id=-1, gamma=0.99, learning_rate=5e-05, batch_size=32, num_steps=1, learner_update_frequency=4, target_update_frequency=10000, start_timesteps=50000, replay_buffer_size=1000000, max_explore_steps=1000000, initial_epsilon=1.0, final_epsilon=0.01, test_epsilon=0.001, num_quantiles=200, kappa=1.0, unroll_steps=1, burn_in_steps=0, reset_rnn_on_terminal=True), quantile_dist_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.distributional_function.QuantileDistributionFunction] = <nnabla_rl.algorithms.qrdqn.DefaultQuantileBuilder object>, quantile_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.qrdqn.DefaultSolverBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.qrdqn.DefaultReplayBufferBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.qrdqn.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
Quantile Regression DQN algorithm.
This class implements the Quantile Regression DQN algorithm proposed by W. Dabney, et al. in the paper: “Distributional Reinforcement Learning with Quantile Regression” For details see: https://arxiv.org/abs/1710.10044
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
QRDQNConfig
) – configuration of QRDQN algorithmquantile_dist_function_builder (
ModelBuilder[QuantileDistributionFunction]
) – builder of quantile distribution function modelsquantile_solver_builder (
SolverBuilder
) – builder for quantile distribution function solversreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_bufferexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_rnn_supported()[source]¶
Check whether the algorithm supports rnn models or not
- Returns
True if the algorithm supports rnn models. Otherwise False.
- Return type
bool
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
Rainbow¶
- class nnabla_rl.algorithms.rainbow.RainbowConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 6.25e-05, batch_size: int = 32, num_steps: int = 3, start_timesteps: int = 20000, replay_buffer_size: int = 1000000, learner_update_frequency: int = 4, target_update_frequency: int = 8000, max_explore_steps: int = 1000000, initial_epsilon: float = 0.0, final_epsilon: float = 0.0, test_epsilon: float = 0.0, v_min: float = - 10.0, v_max: float = 10.0, num_atoms: int = 51, loss_reduction_method: str = 'mean', unroll_steps: int = 1, burn_in_steps: int = 0, reset_rnn_on_terminal: bool = True, alpha: float = 0.5, beta: float = 0.4, betasteps: int = 12500000, warmup_random_steps: int = 0, no_double: bool = False)[source]¶
Bases:
nnabla_rl.algorithms.categorical_ddqn.CategoricalDDQNConfig
List of configurations for Rainbow algorithm.
- Parameters
gamma (float) – discount factor of rewards. Defaults to 0.99.
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.00025 / 4.batch_size (int) – training batch size. Defaults to 32.
start_timesteps (int) – the timestep when training starts. The algorithm will collect experiences from the environment by acting randomly until this timestep. Defaults to 20000.
replay_buffer_size (int) – the capacity of replay buffer. Defaults to 1000000.
learner_update_frequency (float) – the interval of learner update. Defaults to 4.
target_update_frequency (float) – the interval of target q-function update. Defaults to 8000.
v_min (float) – lower limit of the value used in value distribution function. Defaults to -10.0.
v_max (float) – upper limit of the value used in value distribution function. Defaults to 10.0.
num_atoms (int) – the number of bins used in value distribution function. Defaults to 51.
num_steps (int) – the of steps to look ahead in n-step Q learning. Defaults to 3.
alpha (float) – priority exponent (written as omega in the rainbow paper) of prioritized buffer. Defaults to 0.5.
beta (float) – initial value of importance sampling exponent of prioritized buffer. Defaults to 0.4.
betasteps (int) – importance sampling exponent increase steps. After betasteps, exponent will get to 1.0. Defaults to 12500000.
warmup_random_steps (Optional[int]) – steps until this value will NOT use trained policy for exploration. Will explore with randomly selected action. Defaults to 0.
no_double (bool) – If true, following normal Q-learning style q value target will be used for categorical q value update. \(r + \gamma\max_{a}{Q_{\text{target}}(s_{t+1}, a)}\). Defaults to False.
- class nnabla_rl.algorithms.rainbow.Rainbow(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.rainbow.RainbowConfig = RainbowConfig(gpu_id=-1, gamma=0.99, learning_rate=6.25e-05, batch_size=32, num_steps=3, start_timesteps=20000, replay_buffer_size=1000000, learner_update_frequency=4, target_update_frequency=8000, max_explore_steps=1000000, initial_epsilon=0.0, final_epsilon=0.0, test_epsilon=0.0, v_min=-10.0, v_max=10.0, num_atoms=51, loss_reduction_method='mean', unroll_steps=1, burn_in_steps=0, reset_rnn_on_terminal=True, alpha=0.5, beta=0.4, betasteps=12500000, warmup_random_steps=0, no_double=False), value_distribution_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.distributional_function.ValueDistributionFunction] = <nnabla_rl.algorithms.rainbow.DefaultValueDistFunctionBuilder object>, value_distribution_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.rainbow.DefaultSolverBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.rainbow.DefaultReplayBufferBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.rainbow.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithms.categorical_ddqn.CategoricalDDQN
Rainbow algorithm. This class implements the Rainbow algorithm proposed by M. Bellemare, et al. in the paper: “Rainbow: Combining Improvements in Deep Reinforcement Learning” For details see: https://arxiv.org/abs/1710.02298
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
RainbowConfig
) – configuration of the Rainbow algorithmvalue_distribution_builder (
ModelBuilder[ValueDistributionFunctionFunction]
) – builder of value distribution function modelsvalue_distribution_solver_builder (
SolverBuilder
) – builder of value distribution function solversreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_bufferexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
REINFORCE¶
- class nnabla_rl.algorithms.reinforce.REINFORCEConfig(gpu_id: int = - 1, reward_scale: float = 0.01, num_rollouts_per_train_iteration: int = 10, learning_rate: float = 0.001, clip_grad_norm: float = 1.0, fixed_ln_var: float = - 2.3025850929940455)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for REINFORCE algorithm
- Parameters
reward_scale (float) – Scale of reward. Defaults to 0.01.
num_rollouts_per_train_iteration (int) – Number of rollout per each training iteration for collecting on-policy experinces.Increasing this step size is effective to get precise parameters of policy function updating, but computational time of each iteration will increase. Defaults to 10.
learning_rate (float) – Learning rate which is set to the solvers of policy function. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.001.clip_grad_norm (float) – Clip to the norm of gradient to this value. Defaults to 1.0.
fixed_ln_var (float) – Fixed log variance of the policy. This configuration is only valid when the enviroment is continuous. Defaults to 1.0.
- class nnabla_rl.algorithms.reinforce.REINFORCE(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.reinforce.REINFORCEConfig = REINFORCEConfig(gpu_id=-1, reward_scale=0.01, num_rollouts_per_train_iteration=10, learning_rate=0.001, clip_grad_norm=1.0, fixed_ln_var=-2.3025850929940455), policy_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.policy.StochasticPolicy] = <nnabla_rl.algorithms.reinforce.DefaultPolicyBuilder object>, policy_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.reinforce.DefaultSolverBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.reinforce.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
episodic REINFORCE implementation.
This class implements the episodic REINFORCE algorithm proposed by Ronald J. Williams. in the paper: “Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning” For detail see: https://link.springer.com/content/pdf/10.1007/BF00992696.pdf
This algorithm only supports online training.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
REINFORCEConfig
) – configuration of REINFORCE algorithmpolicy_builder (
ModelBuilder[StochasicPolicy]
) – builder for policy function solverspolicy_builder – builder of policy models
explorer_builder (
ExplorerBuilder
) – builder of environment explorer
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
SAC¶
- class nnabla_rl.algorithms.sac.SACConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 0.00030000000000000003, batch_size: int = 256, tau: float = 0.005, environment_steps: int = 1, gradient_steps: int = 1, target_entropy: Optional[float] = None, initial_temperature: Optional[float] = None, fix_temperature: bool = False, start_timesteps: int = 10000, replay_buffer_size: int = 1000000, num_steps: int = 1, actor_unroll_steps: int = 1, actor_burn_in_steps: int = 0, actor_reset_rnn_on_terminal: bool = True, critic_unroll_steps: int = 1, critic_burn_in_steps: int = 0, critic_reset_rnn_on_terminal: bool = True)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for SAC algorithm
- Parameters
gamma (float) – discount factor of rewards. Defaults to 0.99.
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.0003.batch_size (int) – training batch size. Defaults to 256.
tau (float) – target network’s parameter update coefficient. Defaults to 0.005.
environment_steps (int) – Number of steps to interact with the environment on each iteration. Defaults to 1.
gradient_steps (int) – Number of parameter updates to perform on each iteration. Defaults to 1.
target_entropy (float, optional) – Target entropy value. Defaults to None.
initial_temperature (float, optional) – Initial value of temperature parameter. Defaults to None.
fix_temperature (bool) – If true the temperature parameter will not be trained. Defaults to False.
start_timesteps (int) – the timestep when training starts. The algorithm will collect experiences from the environment by acting randomly until this timestep. Defaults to 10000.
replay_buffer_size (int) – capacity of the replay buffer. Defaults to 1000000.
num_steps (int) – number of steps for N-step Q targets. Defaults to 1.
actor_unroll_steps (int) – Number of steps to unroll actor’s tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
actor_burn_in_steps (int) – Number of burn-in steps to initiaze actor’s recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
actor_reset_rnn_on_terminal (bool) – Reset actor’s recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to False.
critic_unroll_steps (int) – Number of steps to unroll critic’s tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
critic_burn_in_steps (int) – Number of burn-in steps to initiaze critic’s recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
critic_reset_rnn_on_terminal (bool) – Reset critic’s recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to False.
- class nnabla_rl.algorithms.sac.SAC(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.sac.SACConfig = SACConfig(gpu_id=-1, gamma=0.99, learning_rate=0.00030000000000000003, batch_size=256, tau=0.005, environment_steps=1, gradient_steps=1, target_entropy=None, initial_temperature=None, fix_temperature=False, start_timesteps=10000, replay_buffer_size=1000000, num_steps=1, actor_unroll_steps=1, actor_burn_in_steps=0, actor_reset_rnn_on_terminal=True, critic_unroll_steps=1, critic_burn_in_steps=0, critic_reset_rnn_on_terminal=True), q_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.q_function.QFunction] = <nnabla_rl.algorithms.sac.DefaultQFunctionBuilder object>, q_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.sac.DefaultSolverBuilder object>, policy_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.policy.StochasticPolicy] = <nnabla_rl.algorithms.sac.DefaultPolicyBuilder object>, policy_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.sac.DefaultSolverBuilder object>, temperature_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.sac.DefaultSolverBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.sac.DefaultReplayBufferBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.sac.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
Soft Actor-Critic (SAC) algorithm implementation.
This class implements the extended version of Soft Actor Critic (SAC) algorithm proposed by T. Haarnoja, et al. in the paper: “Soft Actor-Critic Algorithms and Applications” For detail see: https://arxiv.org/abs/1812.05905
This algorithm is slightly differs from the implementation of Soft Actor-Critic algorithm presented also by T. Haarnoja, et al. in the following paper: https://arxiv.org/abs/1801.01290
The temperature parameter is adjusted automatically instead of providing reward scalar as a hyper parameter.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
SACConfig
) – configuration of the SAC algorithmq_function_builder (
ModelBuilder[QFunction]
) – builder of q function modelsq_solver_builder (
SolverBuilder
) – builder of q function solverspolicy_builder (
ModelBuilder[StochasticPolicy]
) – builder of actor modelspolicy_solver_builder (
SolverBuilder
) – builder of policy solverstemperature_solver_builder (
SolverBuilder
) – builder of temperature solversreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_bufferexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_rnn_supported()[source]¶
Check whether the algorithm supports rnn models or not
- Returns
True if the algorithm supports rnn models. Otherwise False.
- Return type
bool
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
SAC (ICML 2018 version)¶
- class nnabla_rl.algorithms.icml2018_sac.ICML2018SACConfig(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 0.00030000000000000003, batch_size: int = 256, tau: float = 0.005, environment_steps: int = 1, gradient_steps: int = 1, reward_scalar: float = 5.0, start_timesteps: int = 10000, replay_buffer_size: int = 1000000, target_update_interval: int = 1, num_steps: int = 1, pi_unroll_steps: int = 1, pi_burn_in_steps: int = 0, pi_reset_rnn_on_terminal: bool = True, q_unroll_steps: int = 1, q_burn_in_steps: int = 0, q_reset_rnn_on_terminal: bool = True, v_unroll_steps: int = 1, v_burn_in_steps: int = 0, v_reset_rnn_on_terminal: bool = True)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for ICML2018SAC algorithm.
- Parameters
gamma (float) – discount factor of rewards. Defaults to 0.99.
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.0003.batch_size (int) – training batch size. Defaults to 256.
tau (float) – target network’s parameter update coefficient. Defaults to 0.005.
environment_steps (int) – Number of steps to interact with the environment on each iteration. Defaults to 1.
gradient_steps (int) – Number of parameter updates to perform on each iteration. Defaults to 1.
reward_scalar (float) – Reward scaling factor. Obtained reward will be multiplied by this value. Defaults to 5.0.
start_timesteps (int) – the timestep when training starts. The algorithm will collect experiences from the environment by acting randomly until this timestep. Defaults to 10000.
replay_buffer_size (int) – capacity of the replay buffer. Defaults to 1000000.
num_steps (int) – number of steps for N-step Q targets. Defaults to 1.
target_update_interval (float) – the interval of target v function parameter’s update. Defaults to 1.
pi_unroll_steps (int) – Number of steps to unroll policy’s tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
pi_burn_in_steps (int) – Number of burn-in steps to initiaze policy’s recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
pi_reset_rnn_on_terminal (bool) – Reset policy’s recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to False.
q_unroll_steps (int) – Number of steps to unroll q-function’s tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
q_burn_in_steps (int) – Number of burn-in steps to initiaze q-function’s recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
q_reset_rnn_on_terminal (bool) – Reset q-function’s recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to False.
v_unroll_steps (int) – Number of steps to unroll v-function’s tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
v_burn_in_steps (int) – Number of burn-in steps to initiaze v-function’s recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
v_reset_rnn_on_terminal (bool) – Reset v-function’s recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to False.
- class nnabla_rl.algorithms.icml2018_sac.ICML2018SAC(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.icml2018_sac.ICML2018SACConfig = ICML2018SACConfig(gpu_id=-1, gamma=0.99, learning_rate=0.00030000000000000003, batch_size=256, tau=0.005, environment_steps=1, gradient_steps=1, reward_scalar=5.0, start_timesteps=10000, replay_buffer_size=1000000, target_update_interval=1, num_steps=1, pi_unroll_steps=1, pi_burn_in_steps=0, pi_reset_rnn_on_terminal=True, q_unroll_steps=1, q_burn_in_steps=0, q_reset_rnn_on_terminal=True, v_unroll_steps=1, v_burn_in_steps=0, v_reset_rnn_on_terminal=True), v_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.v_function.VFunction] = <nnabla_rl.algorithms.icml2018_sac.DefaultVFunctionBuilder object>, v_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.icml2018_sac.DefaultSolverBuilder object>, q_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.q_function.QFunction] = <nnabla_rl.algorithms.icml2018_sac.DefaultQFunctionBuilder object>, q_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.icml2018_sac.DefaultSolverBuilder object>, policy_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.policy.StochasticPolicy] = <nnabla_rl.algorithms.icml2018_sac.DefaultPolicyBuilder object>, policy_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.icml2018_sac.DefaultSolverBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.icml2018_sac.DefaultReplayBufferBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.icml2018_sac.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
Soft Actor-Critic (SAC) algorithm.
This class implements the ICML2018 version of Soft Actor Critic (SAC) algorithm proposed by T. Haarnoja, et al. in the paper: “Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor” For detail see: https://arxiv.org/abs/1801.01290
This implementation slightly differs from the implementation of Soft Actor-Critic algorithm presented also by T. Haarnoja, et al. in the following paper: https://arxiv.org/abs/1812.05905
You will need to scale the reward received from the environment properly to get the algorithm work.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
ICML2018SACConfig
) – configuration of the ICML2018SAC algorithmv_function_builder (
ModelBuilder[VFunction]
) – builder of v function modelsv_solver_builder (
SolverBuilder
) – builder of v function solversq_function_builder (
ModelBuilder[QFunction]
) – builder of q function modelsq_solver_builder (
SolverBuilder
) – builder of q function solverspolicy_builder (
ModelBuilder[StochasticPolicy]
) – builder of actor modelspolicy_solver_builder (
SolverBuilder
) – builder of policy solversreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_bufferexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_rnn_supported()[source]¶
Check whether the algorithm supports rnn models or not
- Returns
True if the algorithm supports rnn models. Otherwise False.
- Return type
bool
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
TD3¶
- class nnabla_rl.algorithms.td3.TD3Config(gpu_id: int = - 1, gamma: float = 0.99, learning_rate: float = 0.001, batch_size: int = 100, tau: float = 0.005, start_timesteps: int = 10000, replay_buffer_size: int = 1000000, d: int = 2, exploration_noise_sigma: float = 0.1, train_action_noise_sigma: float = 0.2, train_action_noise_abs: float = 0.5, num_steps: int = 1, actor_unroll_steps: int = 1, actor_burn_in_steps: int = 0, actor_reset_rnn_on_terminal: bool = True, critic_unroll_steps: int = 1, critic_burn_in_steps: int = 0, critic_reset_rnn_on_terminal: bool = True)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for TD3 algorithm
- Parameters
gamma (float) – discount factor of rewards. Defaults to 0.99.
learning_rate (float) – learning rate which is set to all solvers. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.003.batch_size (int) – training batch size. Defaults to 100.
tau (float) – target network’s parameter update coefficient. Defaults to 0.005.
start_timesteps (int) – the timestep when training starts. The algorithm will collect experiences from the environment by acting randomly until this timestep. Defaults to 10000.
replay_buffer_size (int) – capacity of the replay buffer. Defaults to 1000000.
d (int) – Interval of the policy update. The policy will be updated every d q-function updates. Defaults to 2.
exploration_noise_sigma (float) – Standard deviation of the gaussian exploration noise. Defaults to 0.1.
train_action_noise_sigma (float) – Standard deviation of the gaussian action noise used in the training. Defaults to 0.2.
train_action_noise_abs (float) – Absolute limit value of action noise used in the training. Defaults to 0.5.
num_steps (int) – number of steps for N-step Q targets. Defaults to 1.
actor_unroll_steps (int) – Number of steps to unroll actor’s tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
actor_burn_in_steps (int) – Number of burn-in steps to initiaze actor’s recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
actor_reset_rnn_on_terminal (bool) – Reset actor’s recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to False.
critic_unroll_steps (int) – Number of steps to unroll critic’s tranining network. The network will be unrolled even though the provided model doesn’t have RNN layers. Defaults to 1.
critic_burn_in_steps (int) – Number of burn-in steps to initiaze critic’s recurrent layer states during training. This flag does not take effect if given model is not an RNN model. Defaults to 0.
critic_reset_rnn_on_terminal (bool) – Reset critic’s recurrent internal states to zero during training if episode ends. This flag does not take effect if given model is not an RNN model. Defaults to False.
- class nnabla_rl.algorithms.td3.TD3(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.td3.TD3Config = TD3Config(gpu_id=-1, gamma=0.99, learning_rate=0.001, batch_size=100, tau=0.005, start_timesteps=10000, replay_buffer_size=1000000, d=2, exploration_noise_sigma=0.1, train_action_noise_sigma=0.2, train_action_noise_abs=0.5, num_steps=1, actor_unroll_steps=1, actor_burn_in_steps=0, actor_reset_rnn_on_terminal=True, critic_unroll_steps=1, critic_burn_in_steps=0, critic_reset_rnn_on_terminal=True), critic_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.q_function.QFunction] = <nnabla_rl.algorithms.td3.DefaultCriticBuilder object>, critic_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.td3.DefaultSolverBuilder object>, actor_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.policy.DeterministicPolicy] = <nnabla_rl.algorithms.td3.DefaultActorBuilder object>, actor_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.td3.DefaultSolverBuilder object>, replay_buffer_builder: nnabla_rl.builders.replay_buffer_builder.ReplayBufferBuilder = <nnabla_rl.algorithms.td3.DefaultReplayBufferBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.td3.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
Twin Delayed Deep Deterministic policy gradient (TD3) algorithm.
This class implements the Twin Delayed Deep Deteministic policy gradien (TD3) algorithm proposed by S. Fujimoto, et al. in the paper: “Addressing Function Approximation Error in Actor-Critic Methods” For detail see: https://arxiv.org/abs/1802.09477
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
TD3Config
) – configuration of the TD3 algorithmcritic_builder (
ModelBuilder[QFunction]
) – builder of critic modelscritic_solver_builder (
SolverBuilder
) – builder of critic solversactor_builder (
ModelBuilder[DeterministicPolicy]
) – builder of actor modelsactor_solver_builder (
SolverBuilder
) – builder of actor solversreplay_buffer_builder (
ReplayBufferBuilder
) – builder of replay_bufferexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_rnn_supported()[source]¶
Check whether the algorithm supports rnn models or not
- Returns
True if the algorithm supports rnn models. Otherwise False.
- Return type
bool
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
TRPO¶
- class nnabla_rl.algorithms.trpo.TRPOConfig(gpu_id: int = - 1, gamma: float = 0.995, lmb: float = 0.97, num_steps_per_iteration: int = 5000, pi_batch_size: int = 5000, sigma_kl_divergence_constraint: float = 0.01, maximum_backtrack_numbers: int = 10, conjugate_gradient_damping: float = 0.1, conjugate_gradient_iterations: int = 20, vf_epochs: int = 5, vf_batch_size: int = 64, vf_learning_rate: float = 0.001, preprocess_state: bool = True, gpu_batch_size: Optional[int] = None)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for TRPO algorithm
- Parameters
gamma (float) – Discount factor of rewards. Defaults to 0.995.
lmb (float) – Scalar of lambda return’s computation in GAE. Defaults to 0.97. This configuration is related to bias and variance of estimated value. If it is close to 0, estimated value is low-variance but biased. If it is close to 1, estimated value is unbiased but high-variance.
num_steps_per_iteration (int) – Number of steps per each training iteration for collecting on-policy experinces. Increasing this step size is effective to get precise parameters of policy and value function updating, but computational time of each iteration will increase. Defaults to 5000.
pi_batch_size (int) – Trainig batch size of policy. Usually, pi_batch_size is the same as num_steps_per_iteration. Defaults to 5000.
sigma_kl_divergence_constraint (float) – Constraint size of kl divergence between previous policy and updated policy. Defaults to 0.01.
maximum_backtrack_numbers (int) – Maximum backtrack numbers of linesearch. Defaults to 10.
conjugate_gradient_damping (float) – Damping size of conjugate gradient method. Defaults to 0.1.
conjugate_gradient_iterations (int) – Number of iterations of conjugate gradient method. Defaults to 20.
vf_epochs (int) – Number of epochs in each iteration. Defaults to 5.
vf_batch_size (int) – Training batch size of value function. Defaults to 64.
vf_learning_rate (float) – Learning rate which is set to the solvers of value function. You can customize/override the learning rate for each solver by implementing the (
SolverBuilder
) by yourself. Defaults to 0.001.preprocess_state (bool) – Enable preprocessing the states in the collected experiences before feeding as training batch. Defaults to True.
gpu_batch_size (int, optional) – Actual batch size to reduce one forward gpu calculation memory. As long as gpu memory size is enough, this configuration should not be specified. If not specified, gpu_batch_size is the same as pi_batch_size. Defaults to None.
- class nnabla_rl.algorithms.trpo.TRPO(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.trpo.TRPOConfig = TRPOConfig(gpu_id=-1, gamma=0.995, lmb=0.97, num_steps_per_iteration=5000, pi_batch_size=5000, sigma_kl_divergence_constraint=0.01, maximum_backtrack_numbers=10, conjugate_gradient_damping=0.1, conjugate_gradient_iterations=20, vf_epochs=5, vf_batch_size=64, vf_learning_rate=0.001, preprocess_state=True, gpu_batch_size=None), v_function_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.v_function.VFunction] = <nnabla_rl.algorithms.trpo.DefaultVFunctionBuilder object>, v_solver_builder: nnabla_rl.builders.solver_builder.SolverBuilder = <nnabla_rl.algorithms.trpo.DefaultSolverBuilder object>, policy_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.policy.StochasticPolicy] = <nnabla_rl.algorithms.trpo.DefaultPolicyBuilder object>, state_preprocessor_builder: typing.Optional[nnabla_rl.builders.preprocessor_builder.PreprocessorBuilder] = <nnabla_rl.algorithms.trpo.DefaultPreprocessorBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.trpo.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
Trust Region Policy Optimiation method with Generalized Advantage Estimation (GAE) implementation.
This class implements the Trust Region Policy Optimiation (TRPO) with Generalized Advantage Estimation (GAE) algorithm proposed by J. Schulman, et al. in the paper: “Trust Region Policy Optimization” and “High-Dimensional Continuous Control Using Generalized Advantage Estimation” For detail see: https://arxiv.org/abs/1502.05477 and https://arxiv.org/abs/1506.02438
This algorithm only supports online training.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
PPOConfig
) – configuration of TRPO algorithmv_function_builder (
ModelBuilder[VFunction]
) – builder of v function modelsv_solver_builder (
SolverBuilder
) – builder for v function solverspolicy_builder (
ModelBuilder[StochasicPolicy]
) – builder of policy modelsstate_preprocessor_builder (None or
PreprocessorBuilder
) – state preprocessor builder to preprocess the statesexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]
TRPO (ICML 2015 version)¶
- class nnabla_rl.algorithms.icml2015_trpo.ICML2015TRPOConfig(gpu_id: int = - 1, gamma: float = 0.99, num_steps_per_iteration: int = 100000, batch_size: int = 100000, gpu_batch_size: Optional[int] = None, sigma_kl_divergence_constraint: float = 0.01, maximum_backtrack_numbers: int = 10, conjugate_gradient_damping: float = 0.001, conjugate_gradient_iterations: int = 10)[source]¶
Bases:
nnabla_rl.algorithm.AlgorithmConfig
List of configurations for ICML2015TRPO algorithm
- Parameters
gamma (float) – Discount factor of rewards. Defaults to 0.99.
num_steps_per_iteration (int) – Number of steps per each training iteration for collecting on-policy experinces. Increasing this step size is effective to get precise parameters of policy and value function updating, but computational time of each iteration will increase. Defaults to 100000.
batch_size (int) – Trainig batch size of policy. Usually, batch_size is the same as num_steps_per_iteration. Defaults to 100000.
gpu_batch_size (int, optional) – Actual batch size to reduce one forward gpu calculation memory. As long as gpu memory size is enough, this configuration should not be specified. If not specified, gpu_batch_size is the same as pi_batch_size. Defaults to None.
sigma_kl_divergence_constraint (float) – Constraint size of kl divergence between previous policy and updated policy. Defaults to 0.01.
maximum_backtrack_numbers (int) – Maximum backtrack numbers of linesearch. Defaults to 10.
conjugate_gradient_damping (float) – Damping size of conjugate gradient method. Defaults to 0.1.
conjugate_gradient_iterations (int) – Number of iterations of conjugate gradient method. Defaults to 20.
- class nnabla_rl.algorithms.icml2015_trpo.ICML2015TRPO(env_or_env_info: typing.Union[gym.core.Env, nnabla_rl.environments.environment_info.EnvironmentInfo], config: nnabla_rl.algorithms.icml2015_trpo.ICML2015TRPOConfig = ICML2015TRPOConfig(gpu_id=-1, gamma=0.99, num_steps_per_iteration=100000, batch_size=100000, gpu_batch_size=None, sigma_kl_divergence_constraint=0.01, maximum_backtrack_numbers=10, conjugate_gradient_damping=0.001, conjugate_gradient_iterations=10), policy_builder: nnabla_rl.builders.model_builder.ModelBuilder[nnabla_rl.models.policy.StochasticPolicy] = <nnabla_rl.algorithms.icml2015_trpo.DefaultPolicyBuilder object>, explorer_builder: nnabla_rl.builders.explorer_builder.ExplorerBuilder = <nnabla_rl.algorithms.icml2015_trpo.DefaultExplorerBuilder object>)[source]¶
Bases:
nnabla_rl.algorithm.Algorithm
Trust Region Policy Optimiation method with Single Path algorithm.
This class implements the Trust Region Policy Optimiation (TRPO) with Single Path algorithm proposed by J. Schulman, et al. in the paper: “Trust Region Policy Optimization” For detail see: https://arxiv.org/abs/1502.05477
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – the environment to train or environment infoconfig (
ICML2015TRPOConfig
) – configuration of ICML2015TRPO algorithmpolicy_builder (
ModelBuilder[StochasicPolicy]
) – builder of policy modelsexplorer_builder (
ExplorerBuilder
) – builder of environment explorer
- compute_eval_action(**kwargs)¶
Compute action for given state using current best policy. This is usually used for evaluation.
- Parameters
state (np.ndarray) – state to compute the action.
begin_of_episode (bool) – Used for rnn state resetting. This flag informs the beginning of episode.
- Returns
Action for given state using current trained policy.
- Return type
np.ndarray
- classmethod is_supported_env(env_or_env_info)[source]¶
Check whether the algorithm supports the enviroment or not.
- Parameters
env_or_env_info (gym.Env or
EnvironmentInfo
) – environment or environment info- Returns
True if the algorithm supports the environment. Otherwise False.
- Return type
bool
- property latest_iteration_state¶
Return latest iteration state that is composed of items of training process state. You can use this state for debugging (e.g. plot loss curve). See [IterationStateHook](./hooks/iteration_state_hook.py) for getting more details.
- Returns
Dictionary with items of training process state.
- Return type
Dict[str, Any]