testbed/DLR-RM__stable-baselines3/stable_baselines3/ddpg/ddpg.py

from typing import Any, Dict, Optional, Type, Union

import torch as th

from stable_baselines3.common.noise import ActionNoise
from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
from stable_baselines3.td3.policies import TD3Policy
from stable_baselines3.td3.td3 import TD3


class DDPG(TD3):
    """
    Deep Deterministic Policy Gradient (DDPG).

    Deterministic Policy Gradient: http://proceedings.mlr.press/v32/silver14.pdf
    DDPG Paper: https://arxiv.org/abs/1509.02971
    Introduction to DDPG: https://spinningup.openai.com/en/latest/algorithms/ddpg.html

    Note: we treat DDPG as a special case of its successor TD3.

    :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
    :param env: The environment to learn from (if registered in Gym, can be str)
    :param learning_rate: learning rate for adam optimizer,
        the same learning rate will be used for all networks (Q-Values, Actor and Value function)
        it can be a function of the current progress remaining (from 1 to 0)
    :param buffer_size: size of the replay buffer
    :param learning_starts: how many steps of the model to collect transitions for before learning starts
    :param batch_size: Minibatch size for each gradient update
    :param tau: the soft update coefficient ("Polyak update", between 0 and 1)
    :param gamma: the discount factor
    :param train_freq: Update the model every ``train_freq`` steps. Set to `-1` to disable.
    :param gradient_steps: How many gradient steps to do after each rollout
        (see ``train_freq`` and ``n_episodes_rollout``)
        Set to ``-1`` means to do as many gradient steps as steps done in the environment
        during the rollout.
    :param n_episodes_rollout: Update the model every ``n_episodes_rollout`` episodes.
        Note that this cannot be used at the same time as ``train_freq``. Set to `-1` to disable.
    :param action_noise: the action noise type (None by default), this can help
        for hard exploration problem. Cf common.noise for the different action noise type.
    :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
        at a cost of more complexity.
        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
    :param create_eval_env: Whether to create a second environment that will be
        used for evaluating the agent periodically. (Only available when passing string for the environment)
    :param policy_kwargs: additional arguments to be passed to the policy on creation
    :param verbose: the verbosity level: 0 no output, 1 info, 2 debug
    :param seed: Seed for the pseudo random generators
    :param device: Device (cpu, cuda, ...) on which the code should be run.
        Setting it to auto, the code will be run on the GPU if possible.
    :param _init_setup_model: Whether or not to build the network at the creation of the instance
    """

    def __init__(
        self,
        policy: Union[str, Type[TD3Policy]],
        env: Union[GymEnv, str],
        learning_rate: Union[float, Schedule] = 1e-3,
        buffer_size: int = int(1e6),
        learning_starts: int = 100,
        batch_size: int = 100,
        tau: float = 0.005,
        gamma: float = 0.99,
        train_freq: int = -1,
        gradient_steps: int = -1,
        n_episodes_rollout: int = 1,
        action_noise: Optional[ActionNoise] = None,
        optimize_memory_usage: bool = False,
        tensorboard_log: Optional[str] = None,
        create_eval_env: bool = False,
        policy_kwargs: Dict[str, Any] = None,
        verbose: int = 0,
        seed: Optional[int] = None,
        device: Union[th.device, str] = "auto",
        _init_setup_model: bool = True,
    ):

        super(DDPG, self).__init__(
            policy=policy,
            env=env,
            learning_rate=learning_rate,
            buffer_size=buffer_size,
            learning_starts=learning_starts,
            batch_size=batch_size,
            tau=tau,
            gamma=gamma,
            train_freq=train_freq,
            gradient_steps=gradient_steps,
            n_episodes_rollout=n_episodes_rollout,
            action_noise=action_noise,
            policy_kwargs=policy_kwargs,
            tensorboard_log=tensorboard_log,
            verbose=verbose,
            device=device,
            create_eval_env=create_eval_env,
            seed=seed,
            optimize_memory_usage=optimize_memory_usage,
            # Remove all tricks from TD3 to obtain DDPG:
            # we still need to specify target_policy_noise > 0 to avoid errors
            policy_delay=1,
            target_noise_clip=0.0,
            target_policy_noise=0.1,
            _init_setup_model=False,
        )

        # Use only one critic
        if "n_critics" not in self.policy_kwargs:
            self.policy_kwargs["n_critics"] = 1

        if _init_setup_model:
            self._setup_model()

    def learn(
        self,
        total_timesteps: int,
        callback: MaybeCallback = None,
        log_interval: int = 4,
        eval_env: Optional[GymEnv] = None,
        eval_freq: int = -1,
        n_eval_episodes: int = 5,
        tb_log_name: str = "DDPG",
        eval_log_path: Optional[str] = None,
        reset_num_timesteps: bool = True,
    ) -> OffPolicyAlgorithm:

        return super(DDPG, self).learn(
            total_timesteps=total_timesteps,
            callback=callback,
            log_interval=log_interval,
            eval_env=eval_env,
            eval_freq=eval_freq,
            n_eval_episodes=n_eval_episodes,
            tb_log_name=tb_log_name,
            eval_log_path=eval_log_path,
            reset_num_timesteps=reset_num_timesteps,
        )