Source code for stable_baselines3.ddpg.ddpg

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

import torch as th

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

SelfDDPG = TypeVar("SelfDDPG", bound="DDPG")

[docs]class DDPG(TD3): """ Deep Deterministic Policy Gradient (DDPG). Deterministic Policy Gradient: DDPG Paper: Introduction to DDPG: 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. Alternatively pass a tuple of frequency and unit like ``(5, "step")`` or ``(2, "episode")``. :param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``) Set to ``-1`` means to do as many gradient steps as steps done in the environment during the rollout. :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 replay_buffer_class: Replay buffer class to use (for instance ``HerReplayBuffer``). If ``None``, it will be automatically selected. :param replay_buffer_kwargs: Keyword arguments to pass to the replay buffer on creation. :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer at a cost of more complexity. See :param policy_kwargs: additional arguments to be passed to the policy on creation :param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for debug messages :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 = 1_000_000, # 1e6 learning_starts: int = 100, batch_size: int = 100, tau: float = 0.005, gamma: float = 0.99, train_freq: Union[int, Tuple[int, str]] = (1, "episode"), gradient_steps: int = -1, action_noise: Optional[ActionNoise] = None, replay_buffer_class: Optional[Type[ReplayBuffer]] = None, replay_buffer_kwargs: Optional[Dict[str, Any]] = None, optimize_memory_usage: bool = False, tensorboard_log: Optional[str] = None, policy_kwargs: Optional[Dict[str, Any]] = None, verbose: int = 0, seed: Optional[int] = None, device: Union[th.device, str] = "auto", _init_setup_model: bool = True, ): super().__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, action_noise=action_noise, replay_buffer_class=replay_buffer_class, replay_buffer_kwargs=replay_buffer_kwargs, policy_kwargs=policy_kwargs, tensorboard_log=tensorboard_log, verbose=verbose, device=device, 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()
[docs] def learn( self: SelfDDPG, total_timesteps: int, callback: MaybeCallback = None, log_interval: int = 4, tb_log_name: str = "DDPG", reset_num_timesteps: bool = True, progress_bar: bool = False, ) -> SelfDDPG: return super().learn( total_timesteps=total_timesteps, callback=callback, log_interval=log_interval, tb_log_name=tb_log_name, reset_num_timesteps=reset_num_timesteps, progress_bar=progress_bar, )