Porting to sb3=2.1.0
This commit is contained in:
@@ -0,0 +1,14 @@
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from sbBrix.ppo import PPO
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from sbBrix.sac import SAC
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try:
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import priorConditionedAnnealing as pca
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except ModuleNotFoundError:
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class pca():
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def PCA_Distribution(*args, **kwargs):
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raise Exception('PCA is not installed; cannot initialize PCA_Distribution.')
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__all__ = [
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"PPO",
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"SAC",
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]
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@@ -0,0 +1,45 @@
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from stable_baselines3.common.distributions import *
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from metastable_baselines2.pca import PCA_Distribution
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def _patched_make_proba_distribution(
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action_space: spaces.Space, use_sde: bool = False, use_pca: bool = False, dist_kwargs: Optional[Dict[str, Any]] = None
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) -> Distribution:
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"""
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Return an instance of Distribution for the correct type of action space
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:param action_space: the input action space
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:param use_sde: Force the use of StateDependentNoiseDistribution
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instead of DiagGaussianDistribution
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:param dist_kwargs: Keyword arguments to pass to the probability distribution
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:return: the appropriate Distribution object
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"""
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assert not (use_sde and use_pca), 'Can not mix sde and pca!'
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if dist_kwargs is None:
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dist_kwargs = {}
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if isinstance(action_space, spaces.Box):
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if use_sde:
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cls = StateDependentNoiseDistribution
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elif use_pca:
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cls = PCA_Distribution
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else:
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cls = DiagGaussianDistribution
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return cls(get_action_dim(action_space), **dist_kwargs)
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elif isinstance(action_space, spaces.Discrete):
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return CategoricalDistribution(action_space.n, **dist_kwargs)
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elif isinstance(action_space, spaces.MultiDiscrete):
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return MultiCategoricalDistribution(list(action_space.nvec), **dist_kwargs)
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elif isinstance(action_space, spaces.MultiBinary):
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return BernoulliDistribution(action_space.n, **dist_kwargs)
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else:
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raise NotImplementedError(
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"Error: probability distribution, not implemented for action space"
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f"of type {type(action_space)}."
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" Must be of type Gym Spaces: Box, Discrete, MultiDiscrete or MultiBinary."
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)
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_orig_make_propa_distribution, make_proba_distribution = make_proba_distribution, _patched_make_proba_distribution
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@@ -0,0 +1,555 @@
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from stable_baselines3.common.off_policy_algorithm import *
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class BetterOffPolicyAlgorithm(OffPolicyAlgorithm):
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"""
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The base for Off-Policy algorithms (ex: SAC/TD3)
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:param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
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:param env: The environment to learn from
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(if registered in Gym, can be str. Can be None for loading trained models)
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:param learning_rate: learning rate for the optimizer,
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it can be a function of the current progress remaining (from 1 to 0)
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:param buffer_size: size of the replay buffer
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:param learning_starts: how many steps of the model to collect transitions for before learning starts
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:param batch_size: Minibatch size for each gradient update
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:param tau: the soft update coefficient ("Polyak update", between 0 and 1)
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:param gamma: the discount factor
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:param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit
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like ``(5, "step")`` or ``(2, "episode")``.
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:param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``)
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Set to ``-1`` means to do as many gradient steps as steps done in the environment
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during the rollout.
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:param action_noise: the action noise type (None by default), this can help
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for hard exploration problem. Cf common.noise for the different action noise type.
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:param replay_buffer_class: Replay buffer class to use (for instance ``HerReplayBuffer``).
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If ``None``, it will be automatically selected.
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:param replay_buffer_kwargs: Keyword arguments to pass to the replay buffer on creation.
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:param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
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at a cost of more complexity.
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See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
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:param policy_kwargs: Additional arguments to be passed to the policy on creation
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:param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
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the reported success rate, mean episode length, and mean reward over
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:param tensorboard_log: the log location for tensorboard (if None, no logging)
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:param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
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debug messages
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:param device: Device on which the code should run.
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By default, it will try to use a Cuda compatible device and fallback to cpu
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if it is not possible.
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:param support_multi_env: Whether the algorithm supports training
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with multiple environments (as in A2C)
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:param monitor_wrapper: When creating an environment, whether to wrap it
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or not in a Monitor wrapper.
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:param seed: Seed for the pseudo random generators
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:param use_sde: Whether to use State Dependent Exploration (SDE)
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instead of action noise exploration (default: False)
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:param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
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Default: -1 (only sample at the beginning of the rollout)
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:param use_sde_at_warmup: Whether to use gSDE instead of uniform sampling
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during the warm up phase (before learning starts)
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:param sde_support: Whether the model support gSDE or not
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:param supported_action_spaces: The action spaces supported by the algorithm.
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"""
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def __init__(
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self,
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policy: Union[str, Type[BasePolicy]],
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env: Union[GymEnv, str],
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learning_rate: Union[float, Schedule],
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buffer_size: int = 1_000_000, # 1e6
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learning_starts: int = 100,
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batch_size: int = 256,
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tau: float = 0.005,
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gamma: float = 0.99,
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train_freq: Union[int, Tuple[int, str]] = (1, "step"),
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gradient_steps: int = 1,
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action_noise: Optional[ActionNoise] = None,
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replay_buffer_class: Optional[Type[ReplayBuffer]] = None,
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replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
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optimize_memory_usage: bool = False,
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policy_kwargs: Optional[Dict[str, Any]] = None,
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stats_window_size: int = 100,
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tensorboard_log: Optional[str] = None,
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verbose: int = 0,
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device: Union[th.device, str] = "auto",
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support_multi_env: bool = False,
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monitor_wrapper: bool = True,
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seed: Optional[int] = None,
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use_sde: bool = False,
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sde_sample_freq: int = -1,
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use_sde_at_warmup: bool = False,
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sde_support: bool = True,
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use_pca: bool = False,
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supported_action_spaces: Optional[Tuple[spaces.Space, ...]] = None,
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):
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assert not (use_sde and use_pca)
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self.use_pca = use_pca
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policy_kwargs["use_pca"] = self.use_pca
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super().__init__(
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policy=policy,
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env=env,
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learning_rate=learning_rate,
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policy_kwargs=policy_kwargs,
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stats_window_size=stats_window_size,
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tensorboard_log=tensorboard_log,
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verbose=verbose,
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device=device,
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support_multi_env=support_multi_env,
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monitor_wrapper=monitor_wrapper,
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seed=seed,
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use_sde=use_sde,
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sde_sample_freq=sde_sample_freq,
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supported_action_spaces=supported_action_spaces,
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buffer_size=buffer_size,
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batch_size=batch_size,
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learning_starts=learning_starts,
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tau=tau,
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gamma=gamma,
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gradient_steps=gradient_steps,
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action_noise=action_noise,
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optimize_memory_usage=optimize_memory_usage,
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replay_buffer_class=replay_buffer_class,
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replay_buffer_kwargs=replay_buffer_kwargs,
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train_freq=train_freq,
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use_sde_at_warmup=use_sde_at_warmup,
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sde_support=sde_support,
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)
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def _convert_train_freq(self) -> None:
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"""
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Convert `train_freq` parameter (int or tuple)
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to a TrainFreq object.
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"""
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if not isinstance(self.train_freq, TrainFreq):
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train_freq = self.train_freq
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# The value of the train frequency will be checked later
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if not isinstance(train_freq, tuple):
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train_freq = (train_freq, "step")
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try:
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train_freq = (train_freq[0], TrainFrequencyUnit(train_freq[1]))
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except ValueError as e:
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raise ValueError(
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f"The unit of the `train_freq` must be either 'step' or 'episode' not '{train_freq[1]}'!"
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) from e
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if not isinstance(train_freq[0], int):
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raise ValueError(f"The frequency of `train_freq` must be an integer and not {train_freq[0]}")
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self.train_freq = TrainFreq(*train_freq)
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def _setup_model(self) -> None:
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self._setup_lr_schedule()
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self.set_random_seed(self.seed)
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if self.replay_buffer_class is None:
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if isinstance(self.observation_space, spaces.Dict):
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self.replay_buffer_class = DictReplayBuffer
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else:
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self.replay_buffer_class = ReplayBuffer
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if self.replay_buffer is None:
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# Make a local copy as we should not pickle
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# the environment when using HerReplayBuffer
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replay_buffer_kwargs = self.replay_buffer_kwargs.copy()
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if issubclass(self.replay_buffer_class, HerReplayBuffer):
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assert self.env is not None, "You must pass an environment when using `HerReplayBuffer`"
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replay_buffer_kwargs["env"] = self.env
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self.replay_buffer = self.replay_buffer_class(
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self.buffer_size,
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self.observation_space,
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self.action_space,
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device=self.device,
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n_envs=self.n_envs,
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optimize_memory_usage=self.optimize_memory_usage,
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**replay_buffer_kwargs, # pytype:disable=wrong-keyword-args
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)
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self.policy = self.policy_class( # pytype:disable=not-instantiable
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self.observation_space,
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self.action_space,
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self.lr_schedule,
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**self.policy_kwargs, # pytype:disable=not-instantiable
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)
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self.policy = self.policy.to(self.device)
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# Convert train freq parameter to TrainFreq object
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self._convert_train_freq()
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def save_replay_buffer(self, path: Union[str, pathlib.Path, io.BufferedIOBase]) -> None:
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"""
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Save the replay buffer as a pickle file.
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:param path: Path to the file where the replay buffer should be saved.
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if path is a str or pathlib.Path, the path is automatically created if necessary.
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"""
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assert self.replay_buffer is not None, "The replay buffer is not defined"
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save_to_pkl(path, self.replay_buffer, self.verbose)
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def load_replay_buffer(
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self,
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path: Union[str, pathlib.Path, io.BufferedIOBase],
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truncate_last_traj: bool = True,
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) -> None:
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"""
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Load a replay buffer from a pickle file.
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:param path: Path to the pickled replay buffer.
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:param truncate_last_traj: When using ``HerReplayBuffer`` with online sampling:
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If set to ``True``, we assume that the last trajectory in the replay buffer was finished
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(and truncate it).
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If set to ``False``, we assume that we continue the same trajectory (same episode).
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"""
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self.replay_buffer = load_from_pkl(path, self.verbose)
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assert isinstance(self.replay_buffer, ReplayBuffer), "The replay buffer must inherit from ReplayBuffer class"
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# Backward compatibility with SB3 < 2.1.0 replay buffer
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# Keep old behavior: do not handle timeout termination separately
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if not hasattr(self.replay_buffer, "handle_timeout_termination"): # pragma: no cover
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self.replay_buffer.handle_timeout_termination = False
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self.replay_buffer.timeouts = np.zeros_like(self.replay_buffer.dones)
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if isinstance(self.replay_buffer, HerReplayBuffer):
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assert self.env is not None, "You must pass an environment at load time when using `HerReplayBuffer`"
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self.replay_buffer.set_env(self.get_env())
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if truncate_last_traj:
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self.replay_buffer.truncate_last_trajectory()
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def _setup_learn(
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self,
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total_timesteps: int,
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callback: MaybeCallback = None,
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reset_num_timesteps: bool = True,
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tb_log_name: str = "run",
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progress_bar: bool = False,
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) -> Tuple[int, BaseCallback]:
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"""
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cf `BaseAlgorithm`.
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"""
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# Prevent continuity issue by truncating trajectory
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# when using memory efficient replay buffer
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# see https://github.com/DLR-RM/stable-baselines3/issues/46
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replay_buffer = self.replay_buffer
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truncate_last_traj = (
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self.optimize_memory_usage
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and reset_num_timesteps
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and replay_buffer is not None
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and (replay_buffer.full or replay_buffer.pos > 0)
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)
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if truncate_last_traj:
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warnings.warn(
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"The last trajectory in the replay buffer will be truncated, "
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"see https://github.com/DLR-RM/stable-baselines3/issues/46."
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"You should use `reset_num_timesteps=False` or `optimize_memory_usage=False`"
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"to avoid that issue."
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)
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# Go to the previous index
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pos = (replay_buffer.pos - 1) % replay_buffer.buffer_size
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replay_buffer.dones[pos] = True
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return super()._setup_learn(
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total_timesteps,
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callback,
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reset_num_timesteps,
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tb_log_name,
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progress_bar,
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)
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def learn(
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self: SelfOffPolicyAlgorithm,
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total_timesteps: int,
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callback: MaybeCallback = None,
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log_interval: int = 4,
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tb_log_name: str = "run",
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reset_num_timesteps: bool = True,
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progress_bar: bool = False,
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) -> SelfOffPolicyAlgorithm:
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total_timesteps, callback = self._setup_learn(
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total_timesteps,
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callback,
|
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reset_num_timesteps,
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tb_log_name,
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progress_bar,
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)
|
||||
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callback.on_training_start(locals(), globals())
|
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|
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while self.num_timesteps < total_timesteps:
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rollout = self.collect_rollouts(
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self.env,
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train_freq=self.train_freq,
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action_noise=self.action_noise,
|
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callback=callback,
|
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learning_starts=self.learning_starts,
|
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replay_buffer=self.replay_buffer,
|
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log_interval=log_interval,
|
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)
|
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|
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if rollout.continue_training is False:
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break
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|
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if self.num_timesteps > 0 and self.num_timesteps > self.learning_starts:
|
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# If no `gradient_steps` is specified,
|
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# do as many gradients steps as steps performed during the rollout
|
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gradient_steps = self.gradient_steps if self.gradient_steps >= 0 else rollout.episode_timesteps
|
||||
# Special case when the user passes `gradient_steps=0`
|
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if gradient_steps > 0:
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self.train(batch_size=self.batch_size, gradient_steps=gradient_steps)
|
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|
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callback.on_training_end()
|
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|
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return self
|
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|
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def train(self, gradient_steps: int, batch_size: int) -> None:
|
||||
"""
|
||||
Sample the replay buffer and do the updates
|
||||
(gradient descent and update target networks)
|
||||
"""
|
||||
raise NotImplementedError()
|
||||
|
||||
def _sample_action(
|
||||
self,
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||||
learning_starts: int,
|
||||
action_noise: Optional[ActionNoise] = None,
|
||||
n_envs: int = 1,
|
||||
) -> Tuple[np.ndarray, np.ndarray]:
|
||||
"""
|
||||
Sample an action according to the exploration policy.
|
||||
This is either done by sampling the probability distribution of the policy,
|
||||
or sampling a random action (from a uniform distribution over the action space)
|
||||
or by adding noise to the deterministic output.
|
||||
|
||||
:param action_noise: Action noise that will be used for exploration
|
||||
Required for deterministic policy (e.g. TD3). This can also be used
|
||||
in addition to the stochastic policy for SAC.
|
||||
:param learning_starts: Number of steps before learning for the warm-up phase.
|
||||
:param n_envs:
|
||||
:return: action to take in the environment
|
||||
and scaled action that will be stored in the replay buffer.
|
||||
The two differs when the action space is not normalized (bounds are not [-1, 1]).
|
||||
"""
|
||||
# Select action randomly or according to policy
|
||||
if self.num_timesteps < learning_starts and not (self.use_sde and self.use_sde_at_warmup):
|
||||
# Warmup phase
|
||||
unscaled_action = np.array([self.action_space.sample() for _ in range(n_envs)])
|
||||
else:
|
||||
# Note: when using continuous actions,
|
||||
# we assume that the policy uses tanh to scale the action
|
||||
# We use non-deterministic action in the case of SAC, for TD3, it does not matter
|
||||
unscaled_action, _ = self.predict(self._last_obs, deterministic=False)
|
||||
|
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# Rescale the action from [low, high] to [-1, 1]
|
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if isinstance(self.action_space, spaces.Box):
|
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scaled_action = self.policy.scale_action(unscaled_action)
|
||||
|
||||
# Add noise to the action (improve exploration)
|
||||
if action_noise is not None:
|
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scaled_action = np.clip(scaled_action + action_noise(), -1, 1)
|
||||
|
||||
# We store the scaled action in the buffer
|
||||
buffer_action = scaled_action
|
||||
action = self.policy.unscale_action(scaled_action)
|
||||
else:
|
||||
# Discrete case, no need to normalize or clip
|
||||
buffer_action = unscaled_action
|
||||
action = buffer_action
|
||||
return action, buffer_action
|
||||
|
||||
def _dump_logs(self) -> None:
|
||||
"""
|
||||
Write log.
|
||||
"""
|
||||
time_elapsed = max((time.time_ns() - self.start_time) / 1e9, sys.float_info.epsilon)
|
||||
fps = int((self.num_timesteps - self._num_timesteps_at_start) / time_elapsed)
|
||||
self.logger.record("time/episodes", self._episode_num, exclude="tensorboard")
|
||||
if len(self.ep_info_buffer) > 0 and len(self.ep_info_buffer[0]) > 0:
|
||||
self.logger.record("rollout/ep_rew_mean", safe_mean([ep_info["r"] for ep_info in self.ep_info_buffer]))
|
||||
self.logger.record("rollout/ep_len_mean", safe_mean([ep_info["l"] for ep_info in self.ep_info_buffer]))
|
||||
self.logger.record("time/fps", fps)
|
||||
self.logger.record("time/time_elapsed", int(time_elapsed), exclude="tensorboard")
|
||||
self.logger.record("time/total_timesteps", self.num_timesteps, exclude="tensorboard")
|
||||
if self.use_sde or self.use_pca:
|
||||
self.logger.record("train/std", (self.actor.get_std()).mean().item())
|
||||
|
||||
if len(self.ep_success_buffer) > 0:
|
||||
self.logger.record("rollout/success_rate", safe_mean(self.ep_success_buffer))
|
||||
# Pass the number of timesteps for tensorboard
|
||||
self.logger.dump(step=self.num_timesteps)
|
||||
|
||||
def _on_step(self) -> None:
|
||||
"""
|
||||
Method called after each step in the environment.
|
||||
It is meant to trigger DQN target network update
|
||||
but can be used for other purposes
|
||||
"""
|
||||
pass
|
||||
|
||||
def _store_transition(
|
||||
self,
|
||||
replay_buffer: ReplayBuffer,
|
||||
buffer_action: np.ndarray,
|
||||
new_obs: Union[np.ndarray, Dict[str, np.ndarray]],
|
||||
reward: np.ndarray,
|
||||
dones: np.ndarray,
|
||||
infos: List[Dict[str, Any]],
|
||||
) -> None:
|
||||
"""
|
||||
Store transition in the replay buffer.
|
||||
We store the normalized action and the unnormalized observation.
|
||||
It also handles terminal observations (because VecEnv resets automatically).
|
||||
|
||||
:param replay_buffer: Replay buffer object where to store the transition.
|
||||
:param buffer_action: normalized action
|
||||
:param new_obs: next observation in the current episode
|
||||
or first observation of the episode (when dones is True)
|
||||
:param reward: reward for the current transition
|
||||
:param dones: Termination signal
|
||||
:param infos: List of additional information about the transition.
|
||||
It may contain the terminal observations and information about timeout.
|
||||
"""
|
||||
# Store only the unnormalized version
|
||||
if self._vec_normalize_env is not None:
|
||||
new_obs_ = self._vec_normalize_env.get_original_obs()
|
||||
reward_ = self._vec_normalize_env.get_original_reward()
|
||||
else:
|
||||
# Avoid changing the original ones
|
||||
self._last_original_obs, new_obs_, reward_ = self._last_obs, new_obs, reward
|
||||
|
||||
# Avoid modification by reference
|
||||
next_obs = deepcopy(new_obs_)
|
||||
# As the VecEnv resets automatically, new_obs is already the
|
||||
# first observation of the next episode
|
||||
for i, done in enumerate(dones):
|
||||
if done and infos[i].get("terminal_observation") is not None:
|
||||
if isinstance(next_obs, dict):
|
||||
next_obs_ = infos[i]["terminal_observation"]
|
||||
# VecNormalize normalizes the terminal observation
|
||||
if self._vec_normalize_env is not None:
|
||||
next_obs_ = self._vec_normalize_env.unnormalize_obs(next_obs_)
|
||||
# Replace next obs for the correct envs
|
||||
for key in next_obs.keys():
|
||||
next_obs[key][i] = next_obs_[key]
|
||||
else:
|
||||
next_obs[i] = infos[i]["terminal_observation"]
|
||||
# VecNormalize normalizes the terminal observation
|
||||
if self._vec_normalize_env is not None:
|
||||
next_obs[i] = self._vec_normalize_env.unnormalize_obs(next_obs[i, :])
|
||||
|
||||
replay_buffer.add(
|
||||
self._last_original_obs,
|
||||
next_obs,
|
||||
buffer_action,
|
||||
reward_,
|
||||
dones,
|
||||
infos,
|
||||
)
|
||||
|
||||
self._last_obs = new_obs
|
||||
# Save the unnormalized observation
|
||||
if self._vec_normalize_env is not None:
|
||||
self._last_original_obs = new_obs_
|
||||
|
||||
def collect_rollouts(
|
||||
self,
|
||||
env: VecEnv,
|
||||
callback: BaseCallback,
|
||||
train_freq: TrainFreq,
|
||||
replay_buffer: ReplayBuffer,
|
||||
action_noise: Optional[ActionNoise] = None,
|
||||
learning_starts: int = 0,
|
||||
log_interval: Optional[int] = None,
|
||||
) -> RolloutReturn:
|
||||
"""
|
||||
Collect experiences and store them into a ``ReplayBuffer``.
|
||||
|
||||
:param env: The training environment
|
||||
:param callback: Callback that will be called at each step
|
||||
(and at the beginning and end of the rollout)
|
||||
:param train_freq: How much experience to collect
|
||||
by doing rollouts of current policy.
|
||||
Either ``TrainFreq(<n>, TrainFrequencyUnit.STEP)``
|
||||
or ``TrainFreq(<n>, TrainFrequencyUnit.EPISODE)``
|
||||
with ``<n>`` being an integer greater than 0.
|
||||
:param action_noise: Action noise that will be used for exploration
|
||||
Required for deterministic policy (e.g. TD3). This can also be used
|
||||
in addition to the stochastic policy for SAC.
|
||||
:param learning_starts: Number of steps before learning for the warm-up phase.
|
||||
:param replay_buffer:
|
||||
:param log_interval: Log data every ``log_interval`` episodes
|
||||
:return:
|
||||
"""
|
||||
# Switch to eval mode (this affects batch norm / dropout)
|
||||
self.policy.set_training_mode(False)
|
||||
|
||||
num_collected_steps, num_collected_episodes = 0, 0
|
||||
|
||||
assert isinstance(env, VecEnv), "You must pass a VecEnv"
|
||||
assert train_freq.frequency > 0, "Should at least collect one step or episode."
|
||||
|
||||
if env.num_envs > 1:
|
||||
assert train_freq.unit == TrainFrequencyUnit.STEP, "You must use only one env when doing episodic training."
|
||||
|
||||
# Vectorize action noise if needed
|
||||
if action_noise is not None and env.num_envs > 1 and not isinstance(action_noise, VectorizedActionNoise):
|
||||
action_noise = VectorizedActionNoise(action_noise, env.num_envs)
|
||||
|
||||
if self.use_sde or self.use_pca:
|
||||
self.actor.reset_noise(env.num_envs)
|
||||
|
||||
callback.on_rollout_start()
|
||||
continue_training = True
|
||||
while should_collect_more_steps(train_freq, num_collected_steps, num_collected_episodes):
|
||||
if (self.use_sde or self.use_pca) and self.sde_sample_freq > 0 and num_collected_steps % self.sde_sample_freq == 0:
|
||||
# Sample a new noise matrix
|
||||
self.actor.reset_noise(env.num_envs)
|
||||
|
||||
# Select action randomly or according to policy
|
||||
actions, buffer_actions = self._sample_action(learning_starts, action_noise, env.num_envs)
|
||||
|
||||
# Rescale and perform action
|
||||
new_obs, rewards, dones, infos = env.step(actions)
|
||||
|
||||
self.num_timesteps += env.num_envs
|
||||
num_collected_steps += 1
|
||||
|
||||
# Give access to local variables
|
||||
callback.update_locals(locals())
|
||||
# Only stop training if return value is False, not when it is None.
|
||||
if callback.on_step() is False:
|
||||
return RolloutReturn(num_collected_steps * env.num_envs, num_collected_episodes, continue_training=False)
|
||||
|
||||
# Retrieve reward and episode length if using Monitor wrapper
|
||||
self._update_info_buffer(infos, dones)
|
||||
|
||||
# Store data in replay buffer (normalized action and unnormalized observation)
|
||||
self._store_transition(replay_buffer, buffer_actions, new_obs, rewards, dones, infos)
|
||||
|
||||
self._update_current_progress_remaining(self.num_timesteps, self._total_timesteps)
|
||||
|
||||
# For DQN, check if the target network should be updated
|
||||
# and update the exploration schedule
|
||||
# For SAC/TD3, the update is dones as the same time as the gradient update
|
||||
# see https://github.com/hill-a/stable-baselines/issues/900
|
||||
self._on_step()
|
||||
|
||||
for idx, done in enumerate(dones):
|
||||
if done:
|
||||
# Update stats
|
||||
num_collected_episodes += 1
|
||||
self._episode_num += 1
|
||||
|
||||
if action_noise is not None:
|
||||
kwargs = dict(indices=[idx]) if env.num_envs > 1 else {}
|
||||
action_noise.reset(**kwargs)
|
||||
|
||||
# Log training infos
|
||||
if log_interval is not None and self._episode_num % log_interval == 0:
|
||||
self._dump_logs()
|
||||
callback.on_rollout_end()
|
||||
|
||||
return RolloutReturn(num_collected_steps * env.num_envs, num_collected_episodes, continue_training)
|
||||
@@ -0,0 +1,265 @@
|
||||
from stable_baselines3.common.on_policy_algorithm import *
|
||||
|
||||
|
||||
class BetterOnPolicyAlgorithm(OnPolicyAlgorithm):
|
||||
"""
|
||||
The base for On-Policy algorithms (ex: A2C/PPO).
|
||||
|
||||
: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: The learning rate, it can be a function
|
||||
of the current progress remaining (from 1 to 0)
|
||||
:param n_steps: The number of steps to run for each environment per update
|
||||
(i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel)
|
||||
:param gamma: Discount factor
|
||||
:param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator.
|
||||
Equivalent to classic advantage when set to 1.
|
||||
:param ent_coef: Entropy coefficient for the loss calculation
|
||||
:param vf_coef: Value function coefficient for the loss calculation
|
||||
:param max_grad_norm: The maximum value for the gradient clipping
|
||||
:param use_sde: Whether to use generalized State Dependent Exploration (gSDE)
|
||||
instead of action noise exploration (default: False)
|
||||
:param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
|
||||
Default: -1 (only sample at the beginning of the rollout)
|
||||
:param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
|
||||
the reported success rate, mean episode length, and mean reward over
|
||||
:param tensorboard_log: the log location for tensorboard (if None, no logging)
|
||||
:param monitor_wrapper: When creating an environment, whether to wrap it
|
||||
or not in a Monitor wrapper.
|
||||
: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
|
||||
:param supported_action_spaces: The action spaces supported by the algorithm.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
policy: Union[str, Type[ActorCriticPolicy]],
|
||||
env: Union[GymEnv, str],
|
||||
learning_rate: Union[float, Schedule],
|
||||
n_steps: int,
|
||||
gamma: float,
|
||||
gae_lambda: float,
|
||||
ent_coef: float,
|
||||
vf_coef: float,
|
||||
max_grad_norm: float,
|
||||
use_sde: bool,
|
||||
sde_sample_freq: int,
|
||||
use_pca: bool,
|
||||
stats_window_size: int = 100,
|
||||
tensorboard_log: Optional[str] = None,
|
||||
monitor_wrapper: bool = True,
|
||||
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,
|
||||
supported_action_spaces: Optional[Tuple[spaces.Space, ...]] = None,
|
||||
):
|
||||
assert not (use_sde and use_pca)
|
||||
self.use_pca = use_pca
|
||||
super().__init__(
|
||||
policy=policy,
|
||||
env=env,
|
||||
learning_rate=learning_rate,
|
||||
n_steps=n_steps,
|
||||
gamma=gamma,
|
||||
gae_lambda=gae_lambda,
|
||||
ent_coef=ent_coef,
|
||||
vf_coef=vf_coef,
|
||||
max_grad_norm=max_grad_norm,
|
||||
policy_kwargs=policy_kwargs,
|
||||
verbose=verbose,
|
||||
device=device,
|
||||
use_sde=use_sde,
|
||||
sde_sample_freq=sde_sample_freq,
|
||||
# support_multi_env=True,
|
||||
seed=seed,
|
||||
stats_window_size=stats_window_size,
|
||||
tensorboard_log=tensorboard_log,
|
||||
supported_action_spaces=supported_action_spaces,
|
||||
monitor_wrapper=monitor_wrapper,
|
||||
_init_setup_model=_init_setup_model
|
||||
)
|
||||
|
||||
def _setup_model(self) -> None:
|
||||
self._setup_lr_schedule()
|
||||
self.set_random_seed(self.seed)
|
||||
|
||||
buffer_cls = DictRolloutBuffer if isinstance(self.observation_space, spaces.Dict) else RolloutBuffer
|
||||
|
||||
self.rollout_buffer = buffer_cls(
|
||||
self.n_steps,
|
||||
self.observation_space,
|
||||
self.action_space,
|
||||
device=self.device,
|
||||
gamma=self.gamma,
|
||||
gae_lambda=self.gae_lambda,
|
||||
n_envs=self.n_envs,
|
||||
)
|
||||
self.policy = self.policy_class( # pytype:disable=not-instantiable
|
||||
self.observation_space,
|
||||
self.action_space,
|
||||
self.lr_schedule,
|
||||
use_sde=self.use_sde,
|
||||
use_pca=self.use_pca,
|
||||
**self.policy_kwargs # pytype:disable=not-instantiable
|
||||
)
|
||||
self.policy = self.policy.to(self.device)
|
||||
|
||||
def collect_rollouts(
|
||||
self,
|
||||
env: VecEnv,
|
||||
callback: BaseCallback,
|
||||
rollout_buffer: RolloutBuffer,
|
||||
n_rollout_steps: int,
|
||||
) -> bool:
|
||||
"""
|
||||
Collect experiences using the current policy and fill a ``RolloutBuffer``.
|
||||
The term rollout here refers to the model-free notion and should not
|
||||
be used with the concept of rollout used in model-based RL or planning.
|
||||
|
||||
:param env: The training environment
|
||||
:param callback: Callback that will be called at each step
|
||||
(and at the beginning and end of the rollout)
|
||||
:param rollout_buffer: Buffer to fill with rollouts
|
||||
:param n_rollout_steps: Number of experiences to collect per environment
|
||||
:return: True if function returned with at least `n_rollout_steps`
|
||||
collected, False if callback terminated rollout prematurely.
|
||||
"""
|
||||
assert self._last_obs is not None, "No previous observation was provided"
|
||||
# Switch to eval mode (this affects batch norm / dropout)
|
||||
self.policy.set_training_mode(False)
|
||||
|
||||
n_steps = 0
|
||||
rollout_buffer.reset()
|
||||
# Sample new weights for the state dependent exploration
|
||||
if self.use_sde:
|
||||
self.policy.reset_noise(env.num_envs)
|
||||
|
||||
callback.on_rollout_start()
|
||||
|
||||
while n_steps < n_rollout_steps:
|
||||
if self.use_sde and self.sde_sample_freq > 0 and n_steps % self.sde_sample_freq == 0:
|
||||
# Sample a new noise matrix
|
||||
self.policy.reset_noise(env.num_envs)
|
||||
|
||||
with th.no_grad():
|
||||
# Convert to pytorch tensor or to TensorDict
|
||||
obs_tensor = obs_as_tensor(self._last_obs, self.device)
|
||||
actions, values, log_probs = self.policy(obs_tensor)
|
||||
actions = actions.cpu().numpy()
|
||||
|
||||
# Rescale and perform action
|
||||
clipped_actions = actions
|
||||
# Clip the actions to avoid out of bound error
|
||||
if isinstance(self.action_space, spaces.Box):
|
||||
clipped_actions = np.clip(actions, self.action_space.low, self.action_space.high)
|
||||
|
||||
new_obs, rewards, dones, infos = env.step(clipped_actions)
|
||||
|
||||
self.num_timesteps += env.num_envs
|
||||
|
||||
# Give access to local variables
|
||||
callback.update_locals(locals())
|
||||
if callback.on_step() is False:
|
||||
return False
|
||||
|
||||
self._update_info_buffer(infos)
|
||||
n_steps += 1
|
||||
|
||||
if isinstance(self.action_space, spaces.Discrete):
|
||||
# Reshape in case of discrete action
|
||||
actions = actions.reshape(-1, 1)
|
||||
|
||||
# Handle timeout by bootstraping with value function
|
||||
# see GitHub issue #633
|
||||
for idx, done in enumerate(dones):
|
||||
if (
|
||||
done
|
||||
and infos[idx].get("terminal_observation") is not None
|
||||
and infos[idx].get("TimeLimit.truncated", False)
|
||||
):
|
||||
terminal_obs = self.policy.obs_to_tensor(infos[idx]["terminal_observation"])[0]
|
||||
with th.no_grad():
|
||||
terminal_value = self.policy.predict_values(terminal_obs)[0]
|
||||
rewards[idx] += self.gamma * terminal_value
|
||||
|
||||
rollout_buffer.add(self._last_obs, actions, rewards, self._last_episode_starts, values, log_probs)
|
||||
self._last_obs = new_obs
|
||||
self._last_episode_starts = dones
|
||||
|
||||
with th.no_grad():
|
||||
# Compute value for the last timestep
|
||||
values = self.policy.predict_values(obs_as_tensor(new_obs, self.device))
|
||||
|
||||
rollout_buffer.compute_returns_and_advantage(last_values=values, dones=dones)
|
||||
|
||||
callback.on_rollout_end()
|
||||
|
||||
return True
|
||||
|
||||
def train(self) -> None:
|
||||
"""
|
||||
Consume current rollout data and update policy parameters.
|
||||
Implemented by individual algorithms.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
def learn(
|
||||
self: SelfOnPolicyAlgorithm,
|
||||
total_timesteps: int,
|
||||
callback: MaybeCallback = None,
|
||||
log_interval: int = 1,
|
||||
tb_log_name: str = "OnPolicyAlgorithm",
|
||||
reset_num_timesteps: bool = True,
|
||||
progress_bar: bool = False,
|
||||
) -> SelfOnPolicyAlgorithm:
|
||||
iteration = 0
|
||||
|
||||
total_timesteps, callback = self._setup_learn(
|
||||
total_timesteps,
|
||||
callback,
|
||||
reset_num_timesteps,
|
||||
tb_log_name,
|
||||
progress_bar,
|
||||
)
|
||||
|
||||
callback.on_training_start(locals(), globals())
|
||||
|
||||
while self.num_timesteps < total_timesteps:
|
||||
continue_training = self.collect_rollouts(self.env, callback, self.rollout_buffer, n_rollout_steps=self.n_steps)
|
||||
|
||||
if continue_training is False:
|
||||
break
|
||||
|
||||
iteration += 1
|
||||
self._update_current_progress_remaining(self.num_timesteps, total_timesteps)
|
||||
|
||||
# Display training infos
|
||||
if log_interval is not None and iteration % log_interval == 0:
|
||||
time_elapsed = max((time.time_ns() - self.start_time) / 1e9, sys.float_info.epsilon)
|
||||
fps = int((self.num_timesteps - self._num_timesteps_at_start) / time_elapsed)
|
||||
self.logger.record("time/iterations", iteration, exclude="tensorboard")
|
||||
if len(self.ep_info_buffer) > 0 and len(self.ep_info_buffer[0]) > 0:
|
||||
self.logger.record("rollout/ep_rew_mean", safe_mean([ep_info["r"] for ep_info in self.ep_info_buffer]))
|
||||
self.logger.record("rollout/ep_len_mean", safe_mean([ep_info["l"] for ep_info in self.ep_info_buffer]))
|
||||
self.logger.record("time/fps", fps)
|
||||
self.logger.record("time/time_elapsed", int(time_elapsed), exclude="tensorboard")
|
||||
self.logger.record("time/total_timesteps", self.num_timesteps, exclude="tensorboard")
|
||||
self.logger.dump(step=self.num_timesteps)
|
||||
|
||||
self.train()
|
||||
|
||||
callback.on_training_end()
|
||||
|
||||
return self
|
||||
|
||||
def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
|
||||
state_dicts = ["policy", "policy.optimizer"]
|
||||
|
||||
return state_dicts, []
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,5 @@
|
||||
from metastable_baselines2.ppo.ppo import PPO
|
||||
|
||||
from stable_baselines3.ppo.policies import CnnPolicy, MlpPolicy, MultiInputPolicy
|
||||
|
||||
__all__ = ["CnnPolicy", "MlpPolicy", "MultiInputPolicy", "PPO"]
|
||||
@@ -0,0 +1,325 @@
|
||||
import warnings
|
||||
from typing import Any, ClassVar, Dict, Optional, Type, TypeVar, Union
|
||||
|
||||
import numpy as np
|
||||
import torch as th
|
||||
from gymnasium import spaces
|
||||
from torch.nn import functional as F
|
||||
|
||||
from stable_baselines3.common.buffers import RolloutBuffer
|
||||
from ..common.on_policy_algorithm import BetterOnPolicyAlgorithm
|
||||
from ..common.policies import ActorCriticPolicy, BasePolicy
|
||||
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
|
||||
from stable_baselines3.common.utils import explained_variance, get_schedule_fn
|
||||
|
||||
SelfPPO = TypeVar("SelfPPO", bound="PPO")
|
||||
|
||||
|
||||
class PPO(BetterOnPolicyAlgorithm):
|
||||
"""
|
||||
Proximal Policy Optimization algorithm (PPO) (clip version)
|
||||
|
||||
Paper: https://arxiv.org/abs/1707.06347
|
||||
Code: This implementation borrows code from OpenAI Spinning Up (https://github.com/openai/spinningup/)
|
||||
https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail and
|
||||
Stable Baselines (PPO2 from https://github.com/hill-a/stable-baselines)
|
||||
|
||||
Introduction to PPO: https://spinningup.openai.com/en/latest/algorithms/ppo.html
|
||||
|
||||
: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: The learning rate, it can be a function
|
||||
of the current progress remaining (from 1 to 0)
|
||||
:param n_steps: The number of steps to run for each environment per update
|
||||
(i.e. rollout buffer size is n_steps * n_envs where n_envs is number of environment copies running in parallel)
|
||||
NOTE: n_steps * n_envs must be greater than 1 (because of the advantage normalization)
|
||||
See https://github.com/pytorch/pytorch/issues/29372
|
||||
:param batch_size: Minibatch size
|
||||
:param n_epochs: Number of epoch when optimizing the surrogate loss
|
||||
:param gamma: Discount factor
|
||||
:param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator
|
||||
:param clip_range: Clipping parameter, it can be a function of the current progress
|
||||
remaining (from 1 to 0).
|
||||
:param clip_range_vf: Clipping parameter for the value function,
|
||||
it can be a function of the current progress remaining (from 1 to 0).
|
||||
This is a parameter specific to the OpenAI implementation. If None is passed (default),
|
||||
no clipping will be done on the value function.
|
||||
IMPORTANT: this clipping depends on the reward scaling.
|
||||
:param normalize_advantage: Whether to normalize or not the advantage
|
||||
:param ent_coef: Entropy coefficient for the loss calculation
|
||||
:param vf_coef: Value function coefficient for the loss calculation
|
||||
:param max_grad_norm: The maximum value for the gradient clipping
|
||||
:param use_sde: Whether to use generalized State Dependent Exploration (gSDE)
|
||||
instead of action noise exploration (default: False)
|
||||
:param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
|
||||
Default: -1 (only sample at the beginning of the rollout)
|
||||
:param use_pca: Wether to use Prior Conditioned Annealing
|
||||
:param rollout_buffer_class: Rollout buffer class to use. If ``None``, it will be automatically selected.
|
||||
:param rollout_buffer_kwargs: Keyword arguments to pass to the rollout buffer on creation
|
||||
:param target_kl: Limit the KL divergence between updates,
|
||||
because the clipping is not enough to prevent large update
|
||||
see issue #213 (cf https://github.com/hill-a/stable-baselines/issues/213)
|
||||
By default, there is no limit on the kl div.
|
||||
:param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
|
||||
the reported success rate, mean episode length, and mean reward over
|
||||
:param tensorboard_log: the log location for tensorboard (if None, no logging)
|
||||
: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
|
||||
"""
|
||||
|
||||
policy_aliases: ClassVar[Dict[str, Type[BasePolicy]]] = {
|
||||
"MlpPolicy": ActorCriticPolicy
|
||||
}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
policy: Union[str, Type[ActorCriticPolicy]],
|
||||
env: Union[GymEnv, str],
|
||||
learning_rate: Union[float, Schedule] = 3e-4,
|
||||
n_steps: int = 2048,
|
||||
batch_size: int = 64,
|
||||
n_epochs: int = 10,
|
||||
gamma: float = 0.99,
|
||||
gae_lambda: float = 0.95,
|
||||
clip_range: Union[float, Schedule] = 0.2,
|
||||
clip_range_vf: Union[None, float, Schedule] = None,
|
||||
normalize_advantage: bool = True,
|
||||
ent_coef: float = 0.0,
|
||||
vf_coef: float = 0.5,
|
||||
max_grad_norm: float = 0.5,
|
||||
use_sde: bool = False,
|
||||
sde_sample_freq: int = -1,
|
||||
use_pca: bool = False,
|
||||
rollout_buffer_class: Optional[Type[RolloutBuffer]] = None,
|
||||
rollout_buffer_kwargs: Optional[Dict[str, Any]] = None,
|
||||
target_kl: Optional[float] = None,
|
||||
stats_window_size: int = 100,
|
||||
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,
|
||||
env,
|
||||
learning_rate=learning_rate,
|
||||
n_steps=n_steps,
|
||||
gamma=gamma,
|
||||
gae_lambda=gae_lambda,
|
||||
ent_coef=ent_coef,
|
||||
vf_coef=vf_coef,
|
||||
max_grad_norm=max_grad_norm,
|
||||
use_sde=use_sde,
|
||||
sde_sample_freq=sde_sample_freq,
|
||||
use_pca=use_pca,
|
||||
rollout_buffer_class=rollout_buffer_class,
|
||||
rollout_buffer_kwargs=rollout_buffer_kwargs,
|
||||
stats_window_size=stats_window_size,
|
||||
tensorboard_log=tensorboard_log,
|
||||
policy_kwargs=policy_kwargs,
|
||||
verbose=verbose,
|
||||
device=device,
|
||||
seed=seed,
|
||||
_init_setup_model=False,
|
||||
supported_action_spaces=(
|
||||
spaces.Box,
|
||||
spaces.Discrete,
|
||||
spaces.MultiDiscrete,
|
||||
spaces.MultiBinary,
|
||||
),
|
||||
)
|
||||
|
||||
print('[i] Using metastable version of PPO')
|
||||
|
||||
# Sanity check, otherwise it will lead to noisy gradient and NaN
|
||||
# because of the advantage normalization
|
||||
if normalize_advantage:
|
||||
assert (
|
||||
batch_size > 1
|
||||
), "`batch_size` must be greater than 1. See https://github.com/DLR-RM/stable-baselines3/issues/440"
|
||||
|
||||
if self.env is not None:
|
||||
# Check that `n_steps * n_envs > 1` to avoid NaN
|
||||
# when doing advantage normalization
|
||||
buffer_size = self.env.num_envs * self.n_steps
|
||||
assert buffer_size > 1 or (
|
||||
not normalize_advantage
|
||||
), f"`n_steps * n_envs` must be greater than 1. Currently n_steps={self.n_steps} and n_envs={self.env.num_envs}"
|
||||
# Check that the rollout buffer size is a multiple of the mini-batch size
|
||||
untruncated_batches = buffer_size // batch_size
|
||||
if buffer_size % batch_size > 0:
|
||||
warnings.warn(
|
||||
f"You have specified a mini-batch size of {batch_size},"
|
||||
f" but because the `RolloutBuffer` is of size `n_steps * n_envs = {buffer_size}`,"
|
||||
f" after every {untruncated_batches} untruncated mini-batches,"
|
||||
f" there will be a truncated mini-batch of size {buffer_size % batch_size}\n"
|
||||
f"We recommend using a `batch_size` that is a factor of `n_steps * n_envs`.\n"
|
||||
f"Info: (n_steps={self.n_steps} and n_envs={self.env.num_envs})"
|
||||
)
|
||||
self.batch_size = batch_size
|
||||
self.n_epochs = n_epochs
|
||||
self.clip_range = clip_range
|
||||
self.clip_range_vf = clip_range_vf
|
||||
self.normalize_advantage = normalize_advantage
|
||||
self.target_kl = target_kl
|
||||
|
||||
if _init_setup_model:
|
||||
self._setup_model()
|
||||
|
||||
def _setup_model(self) -> None:
|
||||
super()._setup_model()
|
||||
|
||||
# Initialize schedules for policy/value clipping
|
||||
self.clip_range = get_schedule_fn(self.clip_range)
|
||||
if self.clip_range_vf is not None:
|
||||
if isinstance(self.clip_range_vf, (float, int)):
|
||||
assert self.clip_range_vf > 0, "`clip_range_vf` must be positive, " "pass `None` to deactivate vf clipping"
|
||||
|
||||
self.clip_range_vf = get_schedule_fn(self.clip_range_vf)
|
||||
|
||||
def train(self) -> None:
|
||||
"""
|
||||
Update policy using the currently gathered rollout buffer.
|
||||
"""
|
||||
# Switch to train mode (this affects batch norm / dropout)
|
||||
self.policy.set_training_mode(True)
|
||||
# Update optimizer learning rate
|
||||
self._update_learning_rate(self.policy.optimizer)
|
||||
# Compute current clip range
|
||||
clip_range = self.clip_range(self._current_progress_remaining)
|
||||
# Optional: clip range for the value function
|
||||
if self.clip_range_vf is not None:
|
||||
clip_range_vf = self.clip_range_vf(self._current_progress_remaining)
|
||||
|
||||
entropy_losses = []
|
||||
pg_losses, value_losses = [], []
|
||||
clip_fractions = []
|
||||
|
||||
continue_training = True
|
||||
# train for n_epochs epochs
|
||||
for epoch in range(self.n_epochs):
|
||||
approx_kl_divs = []
|
||||
# Do a complete pass on the rollout buffer
|
||||
for rollout_data in self.rollout_buffer.get(self.batch_size):
|
||||
actions = rollout_data.actions
|
||||
if isinstance(self.action_space, spaces.Discrete):
|
||||
# Convert discrete action from float to long
|
||||
actions = rollout_data.actions.long().flatten()
|
||||
|
||||
# Re-sample the noise matrix because the log_std has changed
|
||||
if self.use_sde or self.use_pca:
|
||||
self.policy.reset_noise(self.batch_size)
|
||||
|
||||
values, log_prob, entropy = self.policy.evaluate_actions(rollout_data.observations, actions)
|
||||
values = values.flatten()
|
||||
# Normalize advantage
|
||||
advantages = rollout_data.advantages
|
||||
# Normalization does not make sense if mini batchsize == 1, see GH issue #325
|
||||
if self.normalize_advantage and len(advantages) > 1:
|
||||
advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
|
||||
|
||||
# ratio between old and new policy, should be one at the first iteration
|
||||
ratio = th.exp(log_prob - rollout_data.old_log_prob)
|
||||
|
||||
# clipped surrogate loss
|
||||
policy_loss_1 = advantages * ratio
|
||||
policy_loss_2 = advantages * th.clamp(ratio, 1 - clip_range, 1 + clip_range)
|
||||
policy_loss = -th.min(policy_loss_1, policy_loss_2).mean()
|
||||
|
||||
# Logging
|
||||
pg_losses.append(policy_loss.item())
|
||||
clip_fraction = th.mean((th.abs(ratio - 1) > clip_range).float()).item()
|
||||
clip_fractions.append(clip_fraction)
|
||||
|
||||
if self.clip_range_vf is None:
|
||||
# No clipping
|
||||
values_pred = values
|
||||
else:
|
||||
# Clip the difference between old and new value
|
||||
# NOTE: this depends on the reward scaling
|
||||
values_pred = rollout_data.old_values + th.clamp(
|
||||
values - rollout_data.old_values, -clip_range_vf, clip_range_vf
|
||||
)
|
||||
# Value loss using the TD(gae_lambda) target
|
||||
value_loss = F.mse_loss(rollout_data.returns, values_pred)
|
||||
value_losses.append(value_loss.item())
|
||||
|
||||
# Entropy loss favor exploration
|
||||
if entropy is None:
|
||||
# Approximate entropy when no analytical form
|
||||
entropy_loss = -th.mean(-log_prob)
|
||||
else:
|
||||
entropy_loss = -th.mean(entropy)
|
||||
|
||||
entropy_losses.append(entropy_loss.item())
|
||||
|
||||
loss = policy_loss + self.ent_coef * entropy_loss + self.vf_coef * value_loss
|
||||
|
||||
# Calculate approximate form of reverse KL Divergence for early stopping
|
||||
# see issue #417: https://github.com/DLR-RM/stable-baselines3/issues/417
|
||||
# and discussion in PR #419: https://github.com/DLR-RM/stable-baselines3/pull/419
|
||||
# and Schulman blog: http://joschu.net/blog/kl-approx.html
|
||||
with th.no_grad():
|
||||
log_ratio = log_prob - rollout_data.old_log_prob
|
||||
approx_kl_div = th.mean((th.exp(log_ratio) - 1) - log_ratio).cpu().numpy()
|
||||
approx_kl_divs.append(approx_kl_div)
|
||||
|
||||
if self.target_kl is not None and approx_kl_div > 1.5 * self.target_kl:
|
||||
continue_training = False
|
||||
if self.verbose >= 1:
|
||||
print(f"Early stopping at step {epoch} due to reaching max kl: {approx_kl_div:.2f}")
|
||||
break
|
||||
|
||||
# Optimization step
|
||||
self.policy.optimizer.zero_grad()
|
||||
loss.backward()
|
||||
# Clip grad norm
|
||||
th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)
|
||||
self.policy.optimizer.step()
|
||||
|
||||
self._n_updates += 1
|
||||
if not continue_training:
|
||||
break
|
||||
|
||||
explained_var = explained_variance(self.rollout_buffer.values.flatten(), self.rollout_buffer.returns.flatten())
|
||||
|
||||
# Logs
|
||||
self.logger.record("train/entropy_loss", np.mean(entropy_losses))
|
||||
self.logger.record("train/policy_gradient_loss", np.mean(pg_losses))
|
||||
self.logger.record("train/value_loss", np.mean(value_losses))
|
||||
self.logger.record("train/approx_kl", np.mean(approx_kl_divs))
|
||||
self.logger.record("train/clip_fraction", np.mean(clip_fractions))
|
||||
self.logger.record("train/loss", loss.item())
|
||||
self.logger.record("train/explained_variance", explained_var)
|
||||
if hasattr(self.policy, "log_std"):
|
||||
self.logger.record("train/std", th.exp(self.policy.log_std).mean().item())
|
||||
|
||||
self.logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
|
||||
self.logger.record("train/clip_range", clip_range)
|
||||
if self.clip_range_vf is not None:
|
||||
self.logger.record("train/clip_range_vf", clip_range_vf)
|
||||
|
||||
def learn(
|
||||
self: SelfPPO,
|
||||
total_timesteps: int,
|
||||
callback: MaybeCallback = None,
|
||||
log_interval: int = 1,
|
||||
tb_log_name: str = "PPO",
|
||||
reset_num_timesteps: bool = True,
|
||||
progress_bar: bool = False,
|
||||
) -> SelfPPO:
|
||||
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,
|
||||
)
|
||||
@@ -0,0 +1 @@
|
||||
from metastable_baselines2.sac.sac import SAC
|
||||
@@ -0,0 +1,331 @@
|
||||
from typing import Any, ClassVar, Dict, List, Optional, Tuple, Type, TypeVar, Union
|
||||
|
||||
import numpy as np
|
||||
import torch as th
|
||||
from gymnasium import spaces
|
||||
from torch.nn import functional as F
|
||||
|
||||
from stable_baselines3.common.buffers import ReplayBuffer
|
||||
from stable_baselines3.common.noise import ActionNoise
|
||||
# from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
|
||||
from ..common.off_policy_algorithm import BetterOffPolicyAlgorithm
|
||||
from stable_baselines3.common.policies import BasePolicy, ContinuousCritic
|
||||
from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
|
||||
from stable_baselines3.common.utils import get_parameters_by_name, polyak_update
|
||||
from ..common.policies import SACMlpPolicy, SACPolicy, Actor #, CnnPolicy, MlpPolicy, MultiInputPolicy, SACPolicy
|
||||
|
||||
SelfSAC = TypeVar("SelfSAC", bound="SAC")
|
||||
|
||||
|
||||
class SAC(BetterOffPolicyAlgorithm):
|
||||
"""
|
||||
Soft Actor-Critic (SAC)
|
||||
Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,
|
||||
This implementation borrows code from original implementation (https://github.com/haarnoja/sac)
|
||||
from OpenAI Spinning Up (https://github.com/openai/spinningup), from the softlearning repo
|
||||
(https://github.com/rail-berkeley/softlearning/)
|
||||
and from Stable Baselines (https://github.com/hill-a/stable-baselines)
|
||||
Paper: https://arxiv.org/abs/1801.01290
|
||||
Introduction to SAC: https://spinningup.openai.com/en/latest/algorithms/sac.html
|
||||
|
||||
Note: we use double q target and not value target as discussed
|
||||
in https://github.com/hill-a/stable-baselines/issues/270
|
||||
|
||||
: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 ent_coef: Entropy regularization coefficient. (Equivalent to
|
||||
inverse of reward scale in the original SAC paper.) Controlling exploration/exploitation trade-off.
|
||||
Set it to 'auto' to learn it automatically (and 'auto_0.1' for using 0.1 as initial value)
|
||||
:param target_update_interval: update the target network every ``target_network_update_freq``
|
||||
gradient steps.
|
||||
:param target_entropy: target entropy when learning ``ent_coef`` (``ent_coef = 'auto'``)
|
||||
:param use_sde: Whether to use generalized State Dependent Exploration (gSDE)
|
||||
instead of action noise exploration (default: False)
|
||||
:param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
|
||||
Default: -1 (only sample at the beginning of the rollout)
|
||||
:param use_sde_at_warmup: Whether to use gSDE instead of uniform sampling
|
||||
during the warm up phase (before learning starts)
|
||||
:param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
|
||||
the reported success rate, mean episode length, and mean reward over
|
||||
:param tensorboard_log: the log location for tensorboard (if None, no logging)
|
||||
: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
|
||||
"""
|
||||
|
||||
policy_aliases: ClassVar[Dict[str, Type[BasePolicy]]] = {
|
||||
"MlpPolicy": SACMlpPolicy,
|
||||
"SACPolicy": SACPolicy,
|
||||
}
|
||||
|
||||
policy: SACPolicy
|
||||
actor: Actor
|
||||
critic: ContinuousCritic
|
||||
critic_target: ContinuousCritic
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
policy: Union[str, Type[SACPolicy]],
|
||||
env: Union[GymEnv, str],
|
||||
learning_rate: Union[float, Schedule] = 3e-4,
|
||||
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,
|
||||
ent_coef: Union[str, float] = "auto",
|
||||
target_update_interval: int = 1,
|
||||
target_entropy: Union[str, float] = "auto",
|
||||
use_sde: bool = False,
|
||||
sde_sample_freq: int = -1,
|
||||
use_sde_at_warmup: bool = False,
|
||||
use_pca: bool = False,
|
||||
stats_window_size: int = 100,
|
||||
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,
|
||||
env,
|
||||
learning_rate,
|
||||
buffer_size,
|
||||
learning_starts,
|
||||
batch_size,
|
||||
tau,
|
||||
gamma,
|
||||
train_freq,
|
||||
gradient_steps,
|
||||
action_noise,
|
||||
replay_buffer_class=replay_buffer_class,
|
||||
replay_buffer_kwargs=replay_buffer_kwargs,
|
||||
policy_kwargs=policy_kwargs,
|
||||
stats_window_size=stats_window_size,
|
||||
tensorboard_log=tensorboard_log,
|
||||
verbose=verbose,
|
||||
device=device,
|
||||
seed=seed,
|
||||
use_sde=use_sde,
|
||||
sde_sample_freq=sde_sample_freq,
|
||||
use_sde_at_warmup=use_sde_at_warmup,
|
||||
use_pca=use_pca,
|
||||
optimize_memory_usage=optimize_memory_usage,
|
||||
supported_action_spaces=(spaces.Box,),
|
||||
support_multi_env=True,
|
||||
)
|
||||
|
||||
print('[i] Using metastable version of SAC')
|
||||
|
||||
self.target_entropy = target_entropy
|
||||
self.log_ent_coef = None # type: Optional[th.Tensor]
|
||||
# Entropy coefficient / Entropy temperature
|
||||
# Inverse of the reward scale
|
||||
self.ent_coef = ent_coef
|
||||
self.target_update_interval = target_update_interval
|
||||
self.ent_coef_optimizer = Optional[th.optim.Adam] = None
|
||||
|
||||
if _init_setup_model:
|
||||
self._setup_model()
|
||||
|
||||
def _setup_model(self) -> None:
|
||||
super()._setup_model()
|
||||
self._create_aliases()
|
||||
# Running mean and running var
|
||||
self.batch_norm_stats = get_parameters_by_name(self.critic, ["running_"])
|
||||
self.batch_norm_stats_target = get_parameters_by_name(self.critic_target, ["running_"])
|
||||
# Target entropy is used when learning the entropy coefficient
|
||||
if self.target_entropy == "auto":
|
||||
# automatically set target entropy if needed
|
||||
self.target_entropy = float(-np.prod(self.env.action_space.shape).astype(np.float32)) # type: ignore
|
||||
else:
|
||||
# Force conversion
|
||||
# this will also throw an error for unexpected string
|
||||
self.target_entropy = float(self.target_entropy)
|
||||
|
||||
# The entropy coefficient or entropy can be learned automatically
|
||||
# see Automating Entropy Adjustment for Maximum Entropy RL section
|
||||
# of https://arxiv.org/abs/1812.05905
|
||||
if isinstance(self.ent_coef, str) and self.ent_coef.startswith("auto"):
|
||||
# Default initial value of ent_coef when learned
|
||||
init_value = 1.0
|
||||
if "_" in self.ent_coef:
|
||||
init_value = float(self.ent_coef.split("_")[1])
|
||||
assert init_value > 0.0, "The initial value of ent_coef must be greater than 0"
|
||||
|
||||
# Note: we optimize the log of the entropy coeff which is slightly different from the paper
|
||||
# as discussed in https://github.com/rail-berkeley/softlearning/issues/37
|
||||
self.log_ent_coef = th.log(th.ones(1, device=self.device) * init_value).requires_grad_(True)
|
||||
self.ent_coef_optimizer = th.optim.Adam([self.log_ent_coef], lr=self.lr_schedule(1))
|
||||
else:
|
||||
# Force conversion to float
|
||||
# this will throw an error if a malformed string (different from 'auto')
|
||||
# is passed
|
||||
self.ent_coef_tensor = th.tensor(float(self.ent_coef), device=self.device)
|
||||
|
||||
def _create_aliases(self) -> None:
|
||||
self.actor = self.policy.actor
|
||||
self.critic = self.policy.critic
|
||||
self.critic_target = self.policy.critic_target
|
||||
|
||||
def train(self, gradient_steps: int, batch_size: int = 64) -> None:
|
||||
# Switch to train mode (this affects batch norm / dropout)
|
||||
self.policy.set_training_mode(True)
|
||||
# Update optimizers learning rate
|
||||
optimizers = [self.actor.optimizer, self.critic.optimizer]
|
||||
if self.ent_coef_optimizer is not None:
|
||||
optimizers += [self.ent_coef_optimizer]
|
||||
|
||||
# Update learning rate according to lr schedule
|
||||
self._update_learning_rate(optimizers)
|
||||
|
||||
ent_coef_losses, ent_coefs = [], []
|
||||
actor_losses, critic_losses = [], []
|
||||
|
||||
for gradient_step in range(gradient_steps):
|
||||
# Sample replay buffer
|
||||
replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env) # type: ignore[union-attr]
|
||||
|
||||
# We need to sample because `log_std` may have changed between two gradient steps
|
||||
if self.use_sde:
|
||||
self.actor.reset_noise()
|
||||
|
||||
# Action by the current actor for the sampled state
|
||||
actions_pi, log_prob = self.actor.action_log_prob(replay_data.observations)
|
||||
log_prob = log_prob.reshape(-1, 1)
|
||||
|
||||
ent_coef_loss = None
|
||||
if self.ent_coef_optimizer is not None and self.log_ent_coef is not None:
|
||||
# Important: detach the variable from the graph
|
||||
# so we don't change it with other losses
|
||||
# see https://github.com/rail-berkeley/softlearning/issues/60
|
||||
ent_coef = th.exp(self.log_ent_coef.detach())
|
||||
ent_coef_loss = -(self.log_ent_coef * (log_prob + self.target_entropy).detach()).mean()
|
||||
ent_coef_losses.append(ent_coef_loss.item())
|
||||
else:
|
||||
ent_coef = self.ent_coef_tensor
|
||||
|
||||
ent_coefs.append(ent_coef.item())
|
||||
|
||||
# Optimize entropy coefficient, also called
|
||||
# entropy temperature or alpha in the paper
|
||||
if ent_coef_loss is not None:
|
||||
self.ent_coef_optimizer.zero_grad()
|
||||
ent_coef_loss.backward()
|
||||
self.ent_coef_optimizer.step()
|
||||
|
||||
with th.no_grad():
|
||||
# Select action according to policy
|
||||
next_actions, next_log_prob = self.actor.action_log_prob(replay_data.next_observations)
|
||||
# Compute the next Q values: min over all critics targets
|
||||
next_q_values = th.cat(self.critic_target(replay_data.next_observations, next_actions), dim=1)
|
||||
next_q_values, _ = th.min(next_q_values, dim=1, keepdim=True)
|
||||
# add entropy term
|
||||
next_q_values = next_q_values - ent_coef * next_log_prob.reshape(-1, 1)
|
||||
# td error + entropy term
|
||||
target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values
|
||||
|
||||
# Get current Q-values estimates for each critic network
|
||||
# using action from the replay buffer
|
||||
current_q_values = self.critic(replay_data.observations, replay_data.actions)
|
||||
|
||||
# Compute critic loss
|
||||
critic_loss = 0.5 * sum(F.mse_loss(current_q, target_q_values) for current_q in current_q_values)
|
||||
assert isinstance(critic_loss, th.Tensor) # for type checker
|
||||
critic_losses.append(critic_loss.item()) # type: ignore[union-attr]
|
||||
|
||||
# Optimize the critic
|
||||
self.critic.optimizer.zero_grad()
|
||||
critic_loss.backward()
|
||||
self.critic.optimizer.step()
|
||||
|
||||
# Compute actor loss
|
||||
# Alternative: actor_loss = th.mean(log_prob - qf1_pi)
|
||||
# Min over all critic networks
|
||||
q_values_pi = th.cat(self.critic(replay_data.observations, actions_pi), dim=1)
|
||||
min_qf_pi, _ = th.min(q_values_pi, dim=1, keepdim=True)
|
||||
actor_loss = (ent_coef * log_prob - min_qf_pi).mean()
|
||||
actor_losses.append(actor_loss.item())
|
||||
|
||||
# Optimize the actor
|
||||
self.actor.optimizer.zero_grad()
|
||||
actor_loss.backward()
|
||||
self.actor.optimizer.step()
|
||||
|
||||
# Update target networks
|
||||
if gradient_step % self.target_update_interval == 0:
|
||||
polyak_update(self.critic.parameters(), self.critic_target.parameters(), self.tau)
|
||||
# Copy running stats, see GH issue #996
|
||||
polyak_update(self.batch_norm_stats, self.batch_norm_stats_target, 1.0)
|
||||
|
||||
self._n_updates += gradient_steps
|
||||
|
||||
self.logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
|
||||
self.logger.record("train/ent_coef", np.mean(ent_coefs))
|
||||
self.logger.record("train/actor_loss", np.mean(actor_losses))
|
||||
self.logger.record("train/critic_loss", np.mean(critic_losses))
|
||||
if len(ent_coef_losses) > 0:
|
||||
self.logger.record("train/ent_coef_loss", np.mean(ent_coef_losses))
|
||||
|
||||
def learn(
|
||||
self: SelfSAC,
|
||||
total_timesteps: int,
|
||||
callback: MaybeCallback = None,
|
||||
log_interval: int = 4,
|
||||
tb_log_name: str = "SAC",
|
||||
reset_num_timesteps: bool = True,
|
||||
progress_bar: bool = False,
|
||||
) -> SelfSAC:
|
||||
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,
|
||||
)
|
||||
|
||||
def _excluded_save_params(self) -> List[str]:
|
||||
return super()._excluded_save_params() + ["actor", "critic", "critic_target"] # noqa: RUF005
|
||||
|
||||
def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
|
||||
state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
|
||||
if self.ent_coef_optimizer is not None:
|
||||
saved_pytorch_variables = ["log_ent_coef"]
|
||||
state_dicts.append("ent_coef_optimizer")
|
||||
else:
|
||||
saved_pytorch_variables = ["ent_coef_tensor"]
|
||||
return state_dicts, saved_pytorch_variables
|
||||
@@ -0,0 +1,2 @@
|
||||
from stable_baselines3.ppo.policies import CnnPolicy, MlpPolicy, MultiInputPolicy
|
||||
from metastable_baselines2.trpl.trpl import TRPL
|
||||
@@ -0,0 +1 @@
|
||||
pass
|
||||
Reference in New Issue
Block a user