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: 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. 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 https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
: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 = 256,
tau: float = 0.005,
gamma: float = 0.99,
train_freq: Union[int, Tuple[int, str]] = 1,
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,
)