Source code for nnabla_rl.algorithms.munchausen_dqn

# Copyright 2021 Sony Corporation.
# Copyright 2021,2022,2023 Sony Group Corporation.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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from dataclasses import dataclass
from typing import Union

import gym

import nnabla as nn
import nnabla.solvers as NS
import nnabla_rl.model_trainers as MT
from nnabla_rl.algorithms.dqn import (DQN, DefaultExplorerBuilder, DefaultQFunctionBuilder, DefaultReplayBufferBuilder,
from import ExplorerBuilder, ModelBuilder, ReplayBufferBuilder, SolverBuilder
from nnabla_rl.environments.environment_info import EnvironmentInfo
from nnabla_rl.models import QFunction
from nnabla_rl.utils.misc import sync_model

[docs]@dataclass class MunchausenDQNConfig(DQNConfig): """List of configurations for Munchausen DQN algorithm. Args: 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 <>`) 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. """ # Parameters overridden from DQN learning_rate: float = 0.00005 final_epsilon: float = 0.01 test_epsilon: float = 0.001 # munchausen dqn training parameters entropy_temperature: float = 0.03 munchausen_scaling_term: float = 0.9 clipping_value: float = -1 def __post_init__(self): """__post_init__ Check set values are in valid range. """ super().__post_init__() self._assert_positive(self.max_explore_steps, 'max_explore_steps') self._assert_negative(self.clipping_value, 'clipping_value')
class DefaultQSolverBuilder(SolverBuilder): def build_solver(self, # type: ignore[override] env_info: EnvironmentInfo, algorithm_config: MunchausenDQNConfig, **kwargs) -> nn.solvers.Solver: assert isinstance(algorithm_config, MunchausenDQNConfig) return NS.Adam(algorithm_config.learning_rate, eps=1e-2 / algorithm_config.batch_size)
[docs]class MunchausenDQN(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: Args: env_or_env_info\ (gym.Env or :py:class:`EnvironmentInfo <nnabla_rl.environments.environment_info.EnvironmentInfo>`): the environment to train or environment info config (:py:class:`MunchausenDQNConfig <nnabla_rl.algorithms.munchausen_dqn.MunchausenDQNConfig>`): configuration of MunchausenDQN algorithm q_func_builder (:py:class:`ModelBuilder[QFunction] <>`): builder of q-function models q_solver_builder (:py:class:`SolverBuilder <>`): builder for q-function solvers replay_buffer_builder (:py:class:`ReplayBufferBuilder <>`): builder of replay_buffer explorer_builder (:py:class:`ExplorerBuilder <>`): builder of environment explorer """ # type declarations to type check with mypy # NOTE: declared variables are instance variable and NOT class variable, unless it is marked with ClassVar # See for details _config: MunchausenDQNConfig def __init__(self, env_or_env_info: Union[gym.Env, EnvironmentInfo], config: MunchausenDQNConfig = MunchausenDQNConfig(), q_func_builder: ModelBuilder[QFunction] = DefaultQFunctionBuilder(), q_solver_builder: SolverBuilder = DefaultQSolverBuilder(), replay_buffer_builder: ReplayBufferBuilder = DefaultReplayBufferBuilder(), explorer_builder: ExplorerBuilder = DefaultExplorerBuilder()): super(MunchausenDQN, self).__init__(env_or_env_info=env_or_env_info, config=config, q_func_builder=q_func_builder, q_solver_builder=q_solver_builder, replay_buffer_builder=replay_buffer_builder, explorer_builder=explorer_builder) def _setup_q_function_training(self, env_or_buffer): trainer_config = MT.q_value_trainers.MunchausenDQNQTrainerConfig( num_steps=self._config.num_steps, reduction_method='mean', q_loss_scalar=0.5, grad_clip=(-1.0, 1.0), tau=self._config.entropy_temperature, alpha=self._config.munchausen_scaling_term, clip_min=self._config.clipping_value, clip_max=0.0, unroll_steps=self._config.unroll_steps, burn_in_steps=self._config.burn_in_steps, reset_on_terminal=self._config.reset_rnn_on_terminal) q_function_trainer = MT.q_value_trainers.MunchausenDQNQTrainer( train_functions=self._q, solvers={self._q.scope_name: self._q_solver}, target_function=self._target_q, env_info=self._env_info, config=trainer_config) sync_model(self._q, self._target_q) return q_function_trainer