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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,
)
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