# Source code for stable_baselines3.her.her_replay_buffer

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