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https://github.com/saymrwulf/stable-baselines3.git
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f432a6fcdc
* Adding FRASA to the projects page * Updating changelog.rst * Ignore mypy errors for np arrays (python 3.11+) --------- Co-authored-by: Antonin Raffin <antonin.raffin@dlr.de>
408 lines
18 KiB
Python
408 lines
18 KiB
Python
import copy
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import warnings
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from typing import Any, Optional, Union
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import numpy as np
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import torch as th
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from gymnasium import spaces
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from stable_baselines3.common.buffers import DictReplayBuffer
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from stable_baselines3.common.type_aliases import DictReplayBufferSamples
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from stable_baselines3.common.vec_env import VecEnv, VecNormalize
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from stable_baselines3.her.goal_selection_strategy import KEY_TO_GOAL_STRATEGY, GoalSelectionStrategy
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class HerReplayBuffer(DictReplayBuffer):
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"""
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Hindsight Experience Replay (HER) buffer.
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Paper: https://arxiv.org/abs/1707.01495
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Replay buffer for sampling HER (Hindsight Experience Replay) transitions.
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.. note::
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Compared to other implementations, the ``future`` goal sampling strategy is inclusive:
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the current transition can be used when re-sampling.
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:param buffer_size: Max number of element in the buffer
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:param observation_space: Observation space
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:param action_space: Action space
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:param env: The training environment
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:param device: PyTorch device
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:param n_envs: Number of parallel environments
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:param optimize_memory_usage: Enable a memory efficient variant
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Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)
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:param handle_timeout_termination: Handle timeout termination (due to timelimit)
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separately and treat the task as infinite horizon task.
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https://github.com/DLR-RM/stable-baselines3/issues/284
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:param n_sampled_goal: Number of virtual transitions to create per real transition,
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by sampling new goals.
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:param goal_selection_strategy: Strategy for sampling goals for replay.
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One of ['episode', 'final', 'future']
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:param copy_info_dict: Whether to copy the info dictionary and pass it to
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``compute_reward()`` method.
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Please note that the copy may cause a slowdown.
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False by default.
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"""
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env: Optional[VecEnv]
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def __init__(
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self,
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buffer_size: int,
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observation_space: spaces.Dict,
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action_space: spaces.Space,
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env: VecEnv,
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device: Union[th.device, str] = "auto",
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n_envs: int = 1,
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optimize_memory_usage: bool = False,
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handle_timeout_termination: bool = True,
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n_sampled_goal: int = 4,
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goal_selection_strategy: Union[GoalSelectionStrategy, str] = "future",
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copy_info_dict: bool = False,
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):
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super().__init__(
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buffer_size,
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observation_space,
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action_space,
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device=device,
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n_envs=n_envs,
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optimize_memory_usage=optimize_memory_usage,
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handle_timeout_termination=handle_timeout_termination,
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)
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self.env = env
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self.copy_info_dict = copy_info_dict
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# convert goal_selection_strategy into GoalSelectionStrategy if string
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if isinstance(goal_selection_strategy, str):
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self.goal_selection_strategy = KEY_TO_GOAL_STRATEGY[goal_selection_strategy.lower()]
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else:
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self.goal_selection_strategy = goal_selection_strategy
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# check if goal_selection_strategy is valid
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assert isinstance(
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self.goal_selection_strategy, GoalSelectionStrategy
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), f"Invalid goal selection strategy, please use one of {list(GoalSelectionStrategy)}"
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self.n_sampled_goal = n_sampled_goal
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# Compute ratio between HER replays and regular replays in percent
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self.her_ratio = 1 - (1.0 / (self.n_sampled_goal + 1))
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# In some environments, the info dict is used to compute the reward. Then, we need to store it.
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self.infos = np.array([[{} for _ in range(self.n_envs)] for _ in range(self.buffer_size)])
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# To create virtual transitions, we need to know for each transition
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# when an episode starts and ends.
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# We use the following arrays to store the indices,
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# and update them when an episode ends.
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self.ep_start = np.zeros((self.buffer_size, self.n_envs), dtype=np.int64)
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self.ep_length = np.zeros((self.buffer_size, self.n_envs), dtype=np.int64)
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self._current_ep_start = np.zeros(self.n_envs, dtype=np.int64)
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def __getstate__(self) -> dict[str, Any]:
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"""
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Gets state for pickling.
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Excludes self.env, as in general Env's may not be pickleable.
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"""
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state = self.__dict__.copy()
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# these attributes are not pickleable
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del state["env"]
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return state
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def __setstate__(self, state: dict[str, Any]) -> None:
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"""
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Restores pickled state.
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User must call ``set_env()`` after unpickling before using.
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:param state:
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"""
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self.__dict__.update(state)
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assert "env" not in state
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self.env = None
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def set_env(self, env: VecEnv) -> None:
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"""
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Sets the environment.
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:param env:
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"""
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if self.env is not None:
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raise ValueError("Trying to set env of already initialized environment.")
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self.env = env
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def add( # type: ignore[override]
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self,
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obs: dict[str, np.ndarray],
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next_obs: dict[str, np.ndarray],
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action: np.ndarray,
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reward: np.ndarray,
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done: np.ndarray,
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infos: list[dict[str, Any]],
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) -> None:
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# When the buffer is full, we rewrite on old episodes. When we start to
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# rewrite on an old episodes, we want the whole old episode to be deleted
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# (and not only the transition on which we rewrite). To do this, we set
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# the length of the old episode to 0, so it can't be sampled anymore.
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for env_idx in range(self.n_envs):
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episode_start = self.ep_start[self.pos, env_idx]
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episode_length = self.ep_length[self.pos, env_idx]
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if episode_length > 0:
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episode_end = episode_start + episode_length
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episode_indices = np.arange(self.pos, episode_end) % self.buffer_size
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self.ep_length[episode_indices, env_idx] = 0
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# Update episode start
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self.ep_start[self.pos] = self._current_ep_start.copy()
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if self.copy_info_dict:
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self.infos[self.pos] = infos # type: ignore[assignment]
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# Store the transition
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super().add(obs, next_obs, action, reward, done, infos)
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# When episode ends, compute and store the episode length
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for env_idx in range(self.n_envs):
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if done[env_idx]:
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self._compute_episode_length(env_idx)
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def _compute_episode_length(self, env_idx: int) -> None:
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"""
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Compute and store the episode length for environment with index env_idx
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:param env_idx: index of the environment for which the episode length should be computed
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"""
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episode_start = self._current_ep_start[env_idx]
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episode_end = self.pos
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if episode_end < episode_start:
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# Occurs when the buffer becomes full, the storage resumes at the
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# beginning of the buffer. This can happen in the middle of an episode.
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episode_end += self.buffer_size
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episode_indices = np.arange(episode_start, episode_end) % self.buffer_size
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self.ep_length[episode_indices, env_idx] = episode_end - episode_start
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# Update the current episode start
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self._current_ep_start[env_idx] = self.pos
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def sample(self, batch_size: int, env: Optional[VecNormalize] = None) -> DictReplayBufferSamples: # type: ignore[override]
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"""
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Sample elements from the replay buffer.
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:param batch_size: Number of element to sample
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:param env: Associated VecEnv to normalize the observations/rewards when sampling
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:return: Samples
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"""
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# When the buffer is full, we rewrite on old episodes. We don't want to
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# sample incomplete episode transitions, so we have to eliminate some indexes.
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is_valid = self.ep_length > 0
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if not np.any(is_valid):
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raise RuntimeError(
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"Unable to sample before the end of the first episode. We recommend choosing a value "
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"for learning_starts that is greater than the maximum number of timesteps in the environment."
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)
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# Get the indices of valid transitions
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# Example:
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# if is_valid = [[True, False, False], [True, False, True]],
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# is_valid has shape (buffer_size=2, n_envs=3)
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# then valid_indices = [0, 3, 5]
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# they correspond to is_valid[0, 0], is_valid[1, 0] and is_valid[1, 2]
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# or in numpy format ([rows], [columns]): (array([0, 1, 1]), array([0, 0, 2]))
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# Those indices are obtained back using np.unravel_index(valid_indices, is_valid.shape)
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valid_indices = np.flatnonzero(is_valid)
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# Sample valid transitions that will constitute the minibatch of size batch_size
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sampled_indices = np.random.choice(valid_indices, size=batch_size, replace=True)
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# Unravel the indexes, i.e. recover the batch and env indices.
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# Example: if sampled_indices = [0, 3, 5], then batch_indices = [0, 1, 1] and env_indices = [0, 0, 2]
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batch_indices, env_indices = np.unravel_index(sampled_indices, is_valid.shape)
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# Split the indexes between real and virtual transitions.
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nb_virtual = int(self.her_ratio * batch_size)
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virtual_batch_indices, real_batch_indices = np.split(batch_indices, [nb_virtual])
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virtual_env_indices, real_env_indices = np.split(env_indices, [nb_virtual])
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# Get real and virtual data
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real_data = self._get_real_samples(real_batch_indices, real_env_indices, env)
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# Create virtual transitions by sampling new desired goals and computing new rewards
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virtual_data = self._get_virtual_samples(virtual_batch_indices, virtual_env_indices, env)
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# Concatenate real and virtual data
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observations = {
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key: th.cat((real_data.observations[key], virtual_data.observations[key]))
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for key in virtual_data.observations.keys()
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}
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actions = th.cat((real_data.actions, virtual_data.actions))
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next_observations = {
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key: th.cat((real_data.next_observations[key], virtual_data.next_observations[key]))
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for key in virtual_data.next_observations.keys()
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}
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dones = th.cat((real_data.dones, virtual_data.dones))
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rewards = th.cat((real_data.rewards, virtual_data.rewards))
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return DictReplayBufferSamples(
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observations=observations,
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actions=actions,
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next_observations=next_observations,
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dones=dones,
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rewards=rewards,
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)
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def _get_real_samples(
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self,
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batch_indices: np.ndarray,
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env_indices: np.ndarray,
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env: Optional[VecNormalize] = None,
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) -> DictReplayBufferSamples:
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"""
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Get the samples corresponding to the batch and environment indices.
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:param batch_indices: Indices of the transitions
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:param env_indices: Indices of the environments
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:param env: associated gym VecEnv to normalize the
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observations/rewards when sampling, defaults to None
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:return: Samples
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"""
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# Normalize if needed and remove extra dimension (we are using only one env for now)
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obs_ = self._normalize_obs({key: obs[batch_indices, env_indices, :] for key, obs in self.observations.items()}, env)
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next_obs_ = self._normalize_obs(
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{key: obs[batch_indices, env_indices, :] for key, obs in self.next_observations.items()}, env
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)
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assert isinstance(obs_, dict)
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assert isinstance(next_obs_, dict)
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# Convert to torch tensor
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observations = {key: self.to_torch(obs) for key, obs in obs_.items()}
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next_observations = {key: self.to_torch(obs) for key, obs in next_obs_.items()}
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return DictReplayBufferSamples(
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observations=observations,
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actions=self.to_torch(self.actions[batch_indices, env_indices]),
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next_observations=next_observations,
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# Only use dones that are not due to timeouts
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# deactivated by default (timeouts is initialized as an array of False)
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dones=self.to_torch(
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self.dones[batch_indices, env_indices] * (1 - self.timeouts[batch_indices, env_indices])
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).reshape(-1, 1),
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rewards=self.to_torch(self._normalize_reward(self.rewards[batch_indices, env_indices].reshape(-1, 1), env)),
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)
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def _get_virtual_samples(
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self,
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batch_indices: np.ndarray,
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env_indices: np.ndarray,
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env: Optional[VecNormalize] = None,
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) -> DictReplayBufferSamples:
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"""
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Get the samples, sample new desired goals and compute new rewards.
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:param batch_indices: Indices of the transitions
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:param env_indices: Indices of the environments
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:param env: associated gym VecEnv to normalize the
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observations/rewards when sampling, defaults to None
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:return: Samples, with new desired goals and new rewards
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"""
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# Get infos and obs
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obs = {key: obs[batch_indices, env_indices, :] for key, obs in self.observations.items()}
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next_obs = {key: obs[batch_indices, env_indices, :] for key, obs in self.next_observations.items()}
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if self.copy_info_dict:
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# The copy may cause a slow down
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infos = copy.deepcopy(self.infos[batch_indices, env_indices])
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else:
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infos = [{} for _ in range(len(batch_indices))]
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# Sample and set new goals
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new_goals = self._sample_goals(batch_indices, env_indices)
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obs["desired_goal"] = new_goals
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# The desired goal for the next observation must be the same as the previous one
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next_obs["desired_goal"] = new_goals
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assert (
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self.env is not None
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), "You must initialize HerReplayBuffer with a VecEnv so it can compute rewards for virtual transitions"
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# Compute new reward
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rewards = self.env.env_method(
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"compute_reward",
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# the new state depends on the previous state and action
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# s_{t+1} = f(s_t, a_t)
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# so the next achieved_goal depends also on the previous state and action
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# because we are in a GoalEnv:
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# r_t = reward(s_t, a_t) = reward(next_achieved_goal, desired_goal)
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# therefore we have to use next_obs["achieved_goal"] and not obs["achieved_goal"]
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next_obs["achieved_goal"],
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# here we use the new desired goal
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obs["desired_goal"],
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infos,
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# we use the method of the first environment assuming that all environments are identical.
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indices=[0],
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)
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rewards = rewards[0].astype(np.float32) # env_method returns a list containing one element
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obs = self._normalize_obs(obs, env) # type: ignore[assignment]
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next_obs = self._normalize_obs(next_obs, env) # type: ignore[assignment]
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# Convert to torch tensor
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observations = {key: self.to_torch(obs) for key, obs in obs.items()}
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next_observations = {key: self.to_torch(obs) for key, obs in next_obs.items()}
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return DictReplayBufferSamples(
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observations=observations,
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actions=self.to_torch(self.actions[batch_indices, env_indices]),
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next_observations=next_observations,
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# Only use dones that are not due to timeouts
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# deactivated by default (timeouts is initialized as an array of False)
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dones=self.to_torch(
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self.dones[batch_indices, env_indices] * (1 - self.timeouts[batch_indices, env_indices])
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).reshape(-1, 1),
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rewards=self.to_torch(self._normalize_reward(rewards.reshape(-1, 1), env)), # type: ignore[attr-defined]
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)
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def _sample_goals(self, batch_indices: np.ndarray, env_indices: np.ndarray) -> np.ndarray:
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"""
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Sample goals based on goal_selection_strategy.
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:param batch_indices: Indices of the transitions
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:param env_indices: Indices of the environments
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:return: Sampled goals
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"""
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batch_ep_start = self.ep_start[batch_indices, env_indices]
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batch_ep_length = self.ep_length[batch_indices, env_indices]
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if self.goal_selection_strategy == GoalSelectionStrategy.FINAL:
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# Replay with final state of current episode
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transition_indices_in_episode = batch_ep_length - 1
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elif self.goal_selection_strategy == GoalSelectionStrategy.FUTURE:
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# Replay with random state which comes from the same episode and was observed after current transition
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# Note: our implementation is inclusive: current transition can be sampled
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current_indices_in_episode = (batch_indices - batch_ep_start) % self.buffer_size
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transition_indices_in_episode = np.random.randint(current_indices_in_episode, batch_ep_length)
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elif self.goal_selection_strategy == GoalSelectionStrategy.EPISODE:
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# Replay with random state which comes from the same episode as current transition
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transition_indices_in_episode = np.random.randint(0, batch_ep_length)
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else:
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raise ValueError(f"Strategy {self.goal_selection_strategy} for sampling goals not supported!")
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transition_indices = (transition_indices_in_episode + batch_ep_start) % self.buffer_size
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return self.next_observations["achieved_goal"][transition_indices, env_indices]
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def truncate_last_trajectory(self) -> None:
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"""
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If called, we assume that the last trajectory in the replay buffer was finished
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(and truncate it).
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If not called, we assume that we continue the same trajectory (same episode).
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"""
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# If we are at the start of an episode, no need to truncate
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if (self._current_ep_start != self.pos).any():
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warnings.warn(
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"The last trajectory in the replay buffer will be truncated.\n"
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"If you are in the same episode as when the replay buffer was saved,\n"
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"you should use `truncate_last_trajectory=False` to avoid that issue."
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)
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# only consider episodes that are not finished
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for env_idx in np.where(self._current_ep_start != self.pos)[0]:
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# set done = True for last episodes
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self.dones[self.pos - 1, env_idx] = True
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# make sure that last episodes can be sampled and
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# update next episode start (self._current_ep_start)
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self._compute_episode_length(env_idx)
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# handle infinite horizon tasks
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if self.handle_timeout_termination:
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self.timeouts[self.pos - 1, env_idx] = True # not an actual timeout, but it allows bootstrapping
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