Source code for nnabla_rl.algorithms.a2c

# Copyright 2021 Sony Corporation.
# Copyright 2021,2022,2023,2024 Sony Group Corporation.
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import multiprocessing as mp
import os
from collections import namedtuple
from dataclasses import dataclass
from typing import Any, Dict, List, Optional

import gym
import numpy as np

import nnabla as nn
import nnabla_rl.model_trainers as MT
import nnabla_rl.utils.context as context
from nnabla import solvers as NS
from nnabla_rl import environment_explorers as EE
from nnabla_rl.algorithm import Algorithm, AlgorithmConfig, eval_api
from nnabla_rl.algorithms.common_utils import _StochasticPolicyActionSelector
from nnabla_rl.builders import ModelBuilder, SolverBuilder
from nnabla_rl.environments.environment_info import EnvironmentInfo
from nnabla_rl.model_trainers.model_trainer import ModelTrainer, TrainingBatch
from nnabla_rl.models import A3CPolicy, A3CSharedFunctionHead, A3CVFunction, StochasticPolicy, VFunction
from nnabla_rl.utils.data import marshal_experiences, unzip
from nnabla_rl.utils.multiprocess import (
    copy_mp_arrays_to_params,
    copy_params_to_mp_arrays,
    mp_array_from_np_array,
    mp_to_np_array,
    new_mp_arrays_from_params,
    np_to_mp_array,
)
from nnabla_rl.utils.reproductions import set_global_seed
from nnabla_rl.utils.solver_wrappers import AutoClipGradByGlobalNorm


[docs]@dataclass class A2CConfig(AlgorithmConfig): """List of configurations for A2C algorithm. Args: 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 \ (:py:class:`SolverBuilder <nnabla_rl.builders.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 <https://github.com/openai/gym/blob/master/gym/wrappers/time_limit.py>`_.\ Defaults to False. max_grad_norm (Optional[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. """ gamma: float = 0.99 n_steps: int = 5 learning_rate: float = 7e-4 entropy_coefficient: float = 0.01 value_coefficient: float = 0.5 decay: float = 0.99 epsilon: float = 1e-5 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 def __post_init__(self): """__post_init__ Check the set values are in valid range. """ self._assert_between(self.gamma, 0.0, 1.0, "gamma") self._assert_between(self.decay, 0.0, 1.0, "decay") self._assert_positive(self.n_steps, "n_steps") self._assert_positive(self.actor_num, "actor num") self._assert_positive(self.learning_rate, "learning_rate")
class DefaultPolicyBuilder(ModelBuilder[StochasticPolicy]): def build_model( # type: ignore[override] self, scope_name: str, env_info: EnvironmentInfo, algorithm_config: A2CConfig, **kwargs, ) -> StochasticPolicy: _shared_function_head = A3CSharedFunctionHead(scope_name="shared", state_shape=env_info.state_shape) return A3CPolicy( head=_shared_function_head, scope_name="shared", state_shape=env_info.state_shape, action_dim=env_info.action_dim, ) class DefaultVFunctionBuilder(ModelBuilder[VFunction]): def build_model( # type: ignore[override] self, scope_name: str, env_info: EnvironmentInfo, algorithm_config: A2CConfig, **kwargs, ) -> VFunction: _shared_function_head = A3CSharedFunctionHead(scope_name="shared", state_shape=env_info.state_shape) return A3CVFunction(head=_shared_function_head, scope_name="shared", state_shape=env_info.state_shape) class DefaultSolverBuilder(SolverBuilder): def build_solver( # type: ignore[override] self, env_info: EnvironmentInfo, algorithm_config: A2CConfig, **kwargs ) -> nn.solver.Solver: solver = NS.RMSprop( lr=algorithm_config.learning_rate, decay=algorithm_config.decay, eps=algorithm_config.epsilon ) if algorithm_config.max_grad_norm is None: return solver else: return AutoClipGradByGlobalNorm(solver, algorithm_config.max_grad_norm)
[docs]class A2C(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. Args: env_or_env_info\ (gym.Env or :py:class:`EnvironmentInfo <nnabla_rl.environments.environment_info.EnvironmentInfo>`): the environment to train or environment info v_function_builder (:py:class:`ModelBuilder[VFunction] <nnabla_rl.builders.ModelBuilder>`): builder of v function models v_solver_builder (:py:class:`SolverBuilder <nnabla_rl.builders.SolverBuilder>`): builder for v function solvers policy_builder (:py:class:`ModelBuilder[StochasicPolicy] <nnabla_rl.builders.ModelBuilder>`): builder of policy models policy_solver_builder (:py:class:`SolverBuilder <nnabla_rl.builders.SolverBuilder>`): builder for policy solvers config (:py:class:`A2CConfig <nnabla_rl.algorithms.a2c.A2CConfig>`): configuration of A2C algorithm """ # type declarations to type check with mypy # NOTE: declared variables are instance variable and NOT class variable, unless it is marked with ClassVar # See https://mypy.readthedocs.io/en/stable/class_basics.html for details _config: A2CConfig _v_function: VFunction _v_function_solver: nn.solver.Solver _policy: StochasticPolicy _policy_solver: nn.solver.Solver _actors: List["_A2CActor"] _actor_processes: List[mp.Process] _s_current_var: nn.Variable _a_current_var: nn.Variable _returns_var: nn.Variable _policy_trainer: ModelTrainer _v_function_trainer: ModelTrainer _policy_solver_builder: SolverBuilder _v_solver_builder: SolverBuilder _policy_trainer_state: Dict[str, Any] _v_function_trainer_state: Dict[str, Any] _evaluation_actor: _StochasticPolicyActionSelector def __init__( self, env_or_env_info, v_function_builder: ModelBuilder[VFunction] = DefaultVFunctionBuilder(), v_solver_builder: SolverBuilder = DefaultSolverBuilder(), policy_builder: ModelBuilder[StochasticPolicy] = DefaultPolicyBuilder(), policy_solver_builder: SolverBuilder = DefaultSolverBuilder(), config=A2CConfig(), ): super(A2C, self).__init__(env_or_env_info, config=config) # Initialize on cpu and change the context later with nn.context_scope(context.get_nnabla_context(-1)): self._policy = policy_builder("pi", self._env_info, self._config) self._v_function = v_function_builder("v", self._env_info, self._config) self._policy_solver = policy_solver_builder(self._env_info, self._config) self._policy_solver_builder = policy_solver_builder # keep for later use self._v_function_solver = v_solver_builder(self._env_info, self._config) self._v_solver_builder = v_solver_builder # keep for later use self._evaluation_actor = _StochasticPolicyActionSelector( self._env_info, self._policy.shallowcopy(), deterministic=False ) @eval_api def compute_eval_action(self, state, *, begin_of_episode=False, extra_info={}): with nn.context_scope(context.get_nnabla_context(self._config.gpu_id)): action, *_ = self._evaluation_actor(state, begin_of_episode=begin_of_episode) return action def _before_training_start(self, env_or_buffer): if not self._is_env(env_or_buffer): raise ValueError("A2C only supports online training") env = env_or_buffer # FIXME: This setup is a workaround for creating underlying model parameters # If the parameter is not created, the multiprocessable array (created in launch_actor_processes) # will be empty and the agent does not learn anything context.set_nnabla_context(-1) self._setup_policy_training(env) self._setup_v_function_training(env) self._actors, self._actor_processes = self._launch_actor_processes(env) # NOTE: Setting gpu context after the launch of processes # If you set the gpu context before the launch of proceses, the process will corrupt context.set_nnabla_context(self._config.gpu_id) # Setup again here to use gpu (if it is set) old_policy_solver = self._policy_solver self._policy_solver = self._policy_solver_builder(self._env_info, self._config) self._policy_trainer = self._setup_policy_training(env) self._policy_solver.set_states(old_policy_solver.get_states()) old_v_function_solver = self._v_function_solver self._v_function_solver = self._v_solver_builder(self._env_info, self._config) self._v_function_trainer = self._setup_v_function_training(env) self._v_function_solver.set_states(old_v_function_solver.get_states()) def _setup_policy_training(self, env_or_buffer): policy_trainer_config = MT.policy_trainers.A2CPolicyTrainerConfig( entropy_coefficient=self._config.entropy_coefficient ) policy_trainer = MT.policy_trainers.A2CPolicyTrainer( models=self._policy, solvers={self._policy.scope_name: self._policy_solver}, env_info=self._env_info, config=policy_trainer_config, ) return policy_trainer def _setup_v_function_training(self, env_or_buffer): # training input/loss variables v_function_trainer_config = MT.v_value.MonteCarloVTrainerConfig( reduction_method="mean", v_loss_scalar=self._config.value_coefficient ) v_function_trainer = MT.v_value.MonteCarloVTrainer( train_functions=self._v_function, solvers={self._v_function.scope_name: self._v_function_solver}, env_info=self._env_info, config=v_function_trainer_config, ) return v_function_trainer def _launch_actor_processes(self, env): actors = self._build_a2c_actors(env, v_function=self._v_function, policy=self._policy) processes = [] for actor in actors: p = mp.Process(target=actor, daemon=True) p.start() processes.append(p) return actors, processes def _build_a2c_actors(self, env, v_function, policy): actors = [] for i in range(self._config.actor_num): actor = _A2CActor( actor_num=i, env=env, env_info=self._env_info, v_function=v_function, policy=policy, config=self._config ) actors.append(actor) return actors def _after_training_finish(self, env_or_buffer): for actor in self._actors: actor.dispose() for process in self._actor_processes: self._kill_actor_processes(process) def _kill_actor_processes(self, process): process.terminate() process.join() def _run_online_training_iteration(self, env): update_interval = self._config.n_steps * self._config.actor_num if self.iteration_num % update_interval != 0: return experiences = self._collect_experiences(self._actors) self._a2c_training(experiences) def _run_offline_training_iteration(self, buffer): raise NotImplementedError def _collect_experiences(self, actors): for actor in actors: actor.update_v_params(self._v_function.get_parameters()) actor.update_policy_params(self._policy.get_parameters()) actor.run_data_collection() results = [actor.wait_data_collection() for actor in actors] return (np.concatenate(item, axis=0) for item in unzip(results)) def _a2c_training(self, experiences): s, a, returns = experiences advantage = self._compute_advantage(s, returns) extra = {} extra["advantage"] = advantage extra["v_target"] = returns batch = TrainingBatch(batch_size=len(a), s_current=s, a_current=a, extra=extra) # lr decay alpha = self._config.learning_rate if 0 < self._config.learning_rate_decay_iterations: learning_rate_decay = max(1.0 - self._iteration_num / self._config.learning_rate_decay_iterations, 0.0) alpha = alpha * learning_rate_decay self._policy_trainer.set_learning_rate(alpha) self._v_function_trainer.set_learning_rate(alpha) # model update self._policy_trainer_state = self._policy_trainer.train(batch) self._v_function_trainer_state = self._v_function_trainer.train(batch) def _compute_advantage(self, s, returns): if not hasattr(self, "_state_var_for_advantage"): self._state_var_for_advantage = nn.Variable(s.shape) self._returns_var_for_advantage = nn.Variable(returns.shape) v_for_advantage = self._v_function.v(self._state_var_for_advantage) self._advantage = self._returns_var_for_advantage - v_for_advantage self._advantage.need_grad = False self._state_var_for_advantage.d = s self._returns_var_for_advantage.d = returns self._advantage.forward(clear_no_need_grad=True) return self._advantage.d def _models(self): models = {} models[self._policy.scope_name] = self._policy models[self._v_function.scope_name] = self._v_function return models def _solvers(self): solvers = {} solvers[self._policy.scope_name] = self._policy_solver solvers[self._v_function.scope_name] = self._v_function_solver return solvers
[docs] @classmethod def is_supported_env(cls, env_or_env_info): env_info = ( EnvironmentInfo.from_env(env_or_env_info) if isinstance(env_or_env_info, gym.Env) else env_or_env_info ) return not env_info.is_continuous_action_env() and not env_info.is_tuple_action_env()
@property def latest_iteration_state(self): latest_iteration_state = super(A2C, self).latest_iteration_state if hasattr(self, "_policy_trainer_state"): latest_iteration_state["scalar"].update({"pi_loss": float(self._policy_trainer_state["pi_loss"])}) if hasattr(self, "_v_function_trainer_state"): latest_iteration_state["scalar"].update({"v_loss": float(self._v_function_trainer_state["v_loss"])}) return latest_iteration_state @property def trainers(self): return {"v_function": self._v_function_trainer, "policy": self._policy_trainer}
class _A2CActor(object): def __init__(self, actor_num, env, env_info, policy, v_function, config): self._actor_num = actor_num self._env = env self._env_info = env_info self._policy = policy self._v_function = v_function self._n_steps = config.n_steps self._gamma = config.gamma self._config = config # IPC communication variables self._disposed = mp.Value("i", False) self._task_start_event = mp.Event() self._task_finish_event = mp.Event() self._policy_mp_arrays = new_mp_arrays_from_params(policy.get_parameters()) self._v_function_mp_arrays = new_mp_arrays_from_params(v_function.get_parameters()) explorer_config = EE.RawPolicyExplorerConfig( initial_step_num=0, timelimit_as_terminal=self._config.timelimit_as_terminal ) self._environment_explorer = EE.RawPolicyExplorer( policy_action_selector=self._compute_action, env_info=self._env_info, config=explorer_config ) obs_space = self._env.observation_space action_space = self._env.action_space MultiProcessingArrays = namedtuple("MultiProcessingArrays", ["state", "action", "returns"]) state_mp_array_shape = (self._n_steps, *obs_space.shape) state_mp_array = mp_array_from_np_array(np.empty(shape=state_mp_array_shape, dtype=obs_space.dtype)) if env_info.is_discrete_action_env(): action_mp_array_shape = (self._n_steps, 1) action_mp_array = mp_array_from_np_array(np.empty(shape=action_mp_array_shape, dtype=action_space.dtype)) else: action_mp_array_shape = (self._n_steps, action_space.shape[0]) action_mp_array = mp_array_from_np_array(np.empty(shape=action_mp_array_shape, dtype=action_space.dtype)) scalar_mp_array_shape = (self._n_steps, 1) returns_mp_array = mp_array_from_np_array(np.empty(shape=scalar_mp_array_shape, dtype=np.float32)) self._mp_arrays = MultiProcessingArrays( (state_mp_array, state_mp_array_shape, obs_space.dtype), (action_mp_array, action_mp_array_shape, action_space.dtype), (returns_mp_array, scalar_mp_array_shape, np.float32), ) self._exploration_actor = _StochasticPolicyActionSelector(env_info, policy, deterministic=False) def __call__(self): self._run_actor_loop() def dispose(self): self._disposed = True self._task_start_event.set() def run_data_collection(self): self._task_finish_event.clear() self._task_start_event.set() def wait_data_collection(self): self._task_finish_event.wait() return (mp_to_np_array(mp_array, shape, dtype) for (mp_array, shape, dtype) in self._mp_arrays) def update_v_params(self, params): self._update_params(src=params, dest=self._v_function_mp_arrays) def update_policy_params(self, params): self._update_params(src=params, dest=self._policy_mp_arrays) def _run_actor_loop(self): context.set_nnabla_context(self._config.gpu_id) if self._config.seed >= 0: seed = self._actor_num + self._config.seed else: seed = os.getpid() set_global_seed(seed) self._env.seed(seed) while True: self._task_start_event.wait() if self._disposed.get_obj(): break self._synchronize_policy_params(self._policy.get_parameters()) self._synchronize_v_function_params(self._v_function.get_parameters()) experiences = self._run_data_collection() self._fill_result(experiences) self._task_start_event.clear() self._task_finish_event.set() def _run_data_collection(self): experiences = self._environment_explorer.step(self._env, n=self._n_steps, break_if_done=False) s_last = experiences[-1][4] experiences = [(s, a, r, non_terminal) for (s, a, r, non_terminal, *_) in experiences] processed_experiences = self._process_experiences(experiences, s_last) return processed_experiences def _process_experiences(self, experiences, s_last): (s, a, r, non_terminal) = marshal_experiences(experiences) v_last = self._compute_v(s_last) returns = self._compute_returns(r, non_terminal, v_last) return (s, a, returns) def _compute_returns(self, rewards, non_terminals, value_last): returns = [] R = value_last for i, (r, non_terminal) in enumerate(zip(rewards[::-1], non_terminals[::-1])): R = r + self._gamma * R * non_terminal returns.insert(0, [R]) return np.array(returns) def _compute_v(self, s): s = np.expand_dims(s, axis=0) if not hasattr(self, "_state_var"): self._state_var = nn.Variable(s.shape) self._v_var = self._v_function.v(self._state_var) self._v_var.need_grad = False self._state_var.d = s self._v_var.forward(clear_no_need_grad=True) v = self._v_var.d.copy() return v def _fill_result(self, experiences): def array_and_dtype(mp_arrays_item): return mp_arrays_item[0], mp_arrays_item[2] (s, a, returns) = experiences np_to_mp_array(s, *array_and_dtype(self._mp_arrays.state)) np_to_mp_array(a, *array_and_dtype(self._mp_arrays.action)) np_to_mp_array(returns, *array_and_dtype(self._mp_arrays.returns)) @eval_api def _compute_action(self, s, *, begin_of_episode=False): action, info = self._exploration_actor(s, begin_of_episode=begin_of_episode) if self._env_info.is_discrete_action_env(): return np.int32(action), info else: return action, info def _update_params(self, src, dest): copy_params_to_mp_arrays(src, dest) def _synchronize_policy_params(self, params): self._synchronize_params(src=self._policy_mp_arrays, dest=params) def _synchronize_v_function_params(self, params): self._synchronize_params(src=self._v_function_mp_arrays, dest=params) def _synchronize_params(self, src, dest): copy_mp_arrays_to_params(src, dest)