import warnings
from collections import deque
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import torch as th
from stable_baselines3.common.buffers import DictReplayBuffer
from stable_baselines3.common.preprocessing import get_obs_shape
from stable_baselines3.common.type_aliases import DictReplayBufferSamples
from stable_baselines3.common.vec_env import VecEnv, VecNormalize
from stable_baselines3.her.goal_selection_strategy import KEY_TO_GOAL_STRATEGY, GoalSelectionStrategy
def get_time_limit(env: VecEnv, current_max_episode_length: Optional[int]) -> int:
"""
Get time limit from environment.
:param env: Environment from which we want to get the time limit.
:param current_max_episode_length: Current value for max_episode_length.
:return: max episode length
"""
# try to get the attribute from environment
if current_max_episode_length is None:
try:
current_max_episode_length = env.get_attr("spec")[0].max_episode_steps
# Raise the error because the attribute is present but is None
if current_max_episode_length is None:
raise AttributeError
# if not available check if a valid value was passed as an argument
except AttributeError:
raise ValueError(
"The max episode length could not be inferred.\n"
"You must specify a `max_episode_steps` when registering the environment,\n"
"use a `gym.wrappers.TimeLimit` wrapper "
"or pass `max_episode_length` to the model constructor"
)
return current_max_episode_length
[docs]class HerReplayBuffer(DictReplayBuffer):
"""
Hindsight Experience Replay (HER) buffer.
Paper: https://arxiv.org/abs/1707.01495
.. warning::
For performance reasons, the maximum number of steps per episodes must be specified.
In most cases, it will be inferred if you specify ``max_episode_steps`` when registering the environment
or if you use a ``gym.wrappers.TimeLimit`` (and ``env.spec`` is not None).
Otherwise, you can directly pass ``max_episode_length`` to the replay buffer constructor.
Replay buffer for sampling HER (Hindsight Experience Replay) transitions.
In the online sampling case, these new transitions will not be saved in the replay buffer
and will only be created at sampling time.
:param env: The training environment
:param buffer_size: The size of the buffer measured in transitions.
:param max_episode_length: The maximum length of an episode. If not specified,
it will be automatically inferred if the environment uses a ``gym.wrappers.TimeLimit`` wrapper.
:param goal_selection_strategy: Strategy for sampling goals for replay.
One of ['episode', 'final', 'future']
:param device: PyTorch device
:param n_sampled_goal: Number of virtual transitions to create per real transition,
by sampling new goals.
: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
"""
def __init__(
self,
env: VecEnv,
buffer_size: int,
device: Union[th.device, str] = "cpu",
replay_buffer: Optional[DictReplayBuffer] = None,
max_episode_length: Optional[int] = None,
n_sampled_goal: int = 4,
goal_selection_strategy: Union[GoalSelectionStrategy, str] = "future",
online_sampling: bool = True,
handle_timeout_termination: bool = True,
):
super(HerReplayBuffer, self).__init__(buffer_size, env.observation_space, env.action_space, device, env.num_envs)
# 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
# if we sample her transitions online use custom replay buffer
self.online_sampling = online_sampling
# compute ratio between HER replays and regular replays in percent for online HER sampling
self.her_ratio = 1 - (1.0 / (self.n_sampled_goal + 1))
# maximum steps in episode
self.max_episode_length = get_time_limit(env, max_episode_length)
# storage for transitions of current episode for offline sampling
# for online sampling, it replaces the "classic" replay buffer completely
her_buffer_size = buffer_size if online_sampling else self.max_episode_length
self.env = env
self.buffer_size = her_buffer_size
if online_sampling:
replay_buffer = None
self.replay_buffer = replay_buffer
self.online_sampling = online_sampling
# Handle timeouts termination properly if needed
# see https://github.com/DLR-RM/stable-baselines3/issues/284
self.handle_timeout_termination = handle_timeout_termination
# buffer with episodes
# number of episodes which can be stored until buffer size is reached
self.max_episode_stored = self.buffer_size // self.max_episode_length
self.current_idx = 0
# Counter to prevent overflow
self.episode_steps = 0
# Get shape of observation and goal (usually the same)
self.obs_shape = get_obs_shape(self.env.observation_space.spaces["observation"])
self.goal_shape = get_obs_shape(self.env.observation_space.spaces["achieved_goal"])
# input dimensions for buffer initialization
input_shape = {
"observation": (self.env.num_envs,) + self.obs_shape,
"achieved_goal": (self.env.num_envs,) + self.goal_shape,
"desired_goal": (self.env.num_envs,) + self.goal_shape,
"action": (self.action_dim,),
"reward": (1,),
"next_obs": (self.env.num_envs,) + self.obs_shape,
"next_achieved_goal": (self.env.num_envs,) + self.goal_shape,
"next_desired_goal": (self.env.num_envs,) + self.goal_shape,
"done": (1,),
}
self._observation_keys = ["observation", "achieved_goal", "desired_goal"]
self._buffer = {
key: np.zeros((self.max_episode_stored, self.max_episode_length, *dim), dtype=np.float32)
for key, dim in input_shape.items()
}
# Store info dicts are it can be used to compute the reward (e.g. continuity cost)
self.info_buffer = [deque(maxlen=self.max_episode_length) for _ in range(self.max_episode_stored)]
# episode length storage, needed for episodes which has less steps than the maximum length
self.episode_lengths = np.zeros(self.max_episode_stored, 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.
Note: when using offline sampling, this will also save the offline replay buffer.
"""
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
def _get_samples(self, batch_inds: np.ndarray, env: Optional[VecNormalize] = None) -> DictReplayBufferSamples:
"""
Abstract method from base class.
"""
raise NotImplementedError()
[docs] def sample(
self,
batch_size: int,
env: Optional[VecNormalize],
) -> DictReplayBufferSamples:
"""
Sample function for online sampling of HER transition,
this replaces the "regular" replay buffer ``sample()``
method in the ``train()`` function.
:param batch_size: Number of element to sample
:param env: Associated gym VecEnv
to normalize the observations/rewards when sampling
:return: Samples.
"""
if self.replay_buffer is not None:
return self.replay_buffer.sample(batch_size, env)
return self._sample_transitions(batch_size, maybe_vec_env=env, online_sampling=True) # pytype: disable=bad-return-type
def _sample_offline(
self,
n_sampled_goal: Optional[int] = None,
) -> Tuple[Dict[str, np.ndarray], Dict[str, np.ndarray], np.ndarray, np.ndarray]:
"""
Sample function for offline sampling of HER transition,
in that case, only one episode is used and transitions
are added to the regular replay buffer.
:param n_sampled_goal: Number of sampled goals for replay
:return: at most(n_sampled_goal * episode_length) HER transitions.
"""
# `maybe_vec_env=None` as we should store unnormalized transitions,
# they will be normalized at sampling time
return self._sample_transitions(
batch_size=None,
maybe_vec_env=None,
online_sampling=False,
n_sampled_goal=n_sampled_goal,
)
[docs] def sample_goals(
self,
episode_indices: np.ndarray,
her_indices: np.ndarray,
transitions_indices: np.ndarray,
) -> np.ndarray:
"""
Sample goals based on goal_selection_strategy.
This is a vectorized (fast) version.
:param episode_indices: Episode indices to use.
:param her_indices: HER indices.
:param transitions_indices: Transition indices to use.
:return: Return sampled goals.
"""
her_episode_indices = episode_indices[her_indices]
if self.goal_selection_strategy == GoalSelectionStrategy.FINAL:
# replay with final state of current episode
transitions_indices = self.episode_lengths[her_episode_indices] - 1
elif self.goal_selection_strategy == GoalSelectionStrategy.FUTURE:
# replay with random state which comes from the same episode and was observed after current transition
transitions_indices = np.random.randint(
transitions_indices[her_indices] + 1, self.episode_lengths[her_episode_indices]
)
elif self.goal_selection_strategy == GoalSelectionStrategy.EPISODE:
# replay with random state which comes from the same episode as current transition
transitions_indices = np.random.randint(self.episode_lengths[her_episode_indices])
else:
raise ValueError(f"Strategy {self.goal_selection_strategy} for sampling goals not supported!")
return self._buffer["achieved_goal"][her_episode_indices, transitions_indices]
def _sample_transitions(
self,
batch_size: Optional[int],
maybe_vec_env: Optional[VecNormalize],
online_sampling: bool,
n_sampled_goal: Optional[int] = None,
) -> Union[DictReplayBufferSamples, Tuple[Dict[str, np.ndarray], Dict[str, np.ndarray], np.ndarray, np.ndarray]]:
"""
:param batch_size: Number of element to sample (only used for online sampling)
:param env: associated gym VecEnv to normalize the observations/rewards
Only valid when using online sampling
:param online_sampling: Using online_sampling for HER or not.
:param n_sampled_goal: Number of sampled goals for replay. (offline sampling)
:return: Samples.
"""
# Select which episodes to use
if online_sampling:
assert batch_size is not None, "No batch_size specified for online sampling of HER transitions"
# Do not sample the episode with index `self.pos` as the episode is invalid
if self.full:
episode_indices = (
np.random.randint(1, self.n_episodes_stored, batch_size) + self.pos
) % self.n_episodes_stored
else:
episode_indices = np.random.randint(0, self.n_episodes_stored, batch_size)
# A subset of the transitions will be relabeled using HER algorithm
her_indices = np.arange(batch_size)[: int(self.her_ratio * batch_size)]
else:
assert maybe_vec_env is None, "Transitions must be stored unnormalized in the replay buffer"
assert n_sampled_goal is not None, "No n_sampled_goal specified for offline sampling of HER transitions"
# Offline sampling: there is only one episode stored
episode_length = self.episode_lengths[0]
# we sample n_sampled_goal per timestep in the episode (only one is stored).
episode_indices = np.tile(0, (episode_length * n_sampled_goal))
# we only sample virtual transitions
# as real transitions are already stored in the replay buffer
her_indices = np.arange(len(episode_indices))
ep_lengths = self.episode_lengths[episode_indices]
# Special case when using the "future" goal sampling strategy
# we cannot sample all transitions, we have to remove the last timestep
if self.goal_selection_strategy == GoalSelectionStrategy.FUTURE:
# restrict the sampling domain when ep_lengths > 1
# otherwise filter out the indices
her_indices = her_indices[ep_lengths[her_indices] > 1]
ep_lengths[her_indices] -= 1
if online_sampling:
# Select which transitions to use
transitions_indices = np.random.randint(ep_lengths)
else:
if her_indices.size == 0:
# Episode of one timestep, not enough for using the "future" strategy
# no virtual transitions are created in that case
return {}, {}, np.zeros(0), np.zeros(0)
else:
# Repeat every transition index n_sampled_goals times
# to sample n_sampled_goal per timestep in the episode (only one is stored).
# Now with the corrected episode length when using "future" strategy
transitions_indices = np.tile(np.arange(ep_lengths[0]), n_sampled_goal)
episode_indices = episode_indices[transitions_indices]
her_indices = np.arange(len(episode_indices))
# get selected transitions
transitions = {key: self._buffer[key][episode_indices, transitions_indices].copy() for key in self._buffer.keys()}
# sample new desired goals and relabel the transitions
new_goals = self.sample_goals(episode_indices, her_indices, transitions_indices)
transitions["desired_goal"][her_indices] = new_goals
# Convert info buffer to numpy array
transitions["info"] = np.array(
[
self.info_buffer[episode_idx][transition_idx]
for episode_idx, transition_idx in zip(episode_indices, transitions_indices)
]
)
# Edge case: episode of one timesteps with the future strategy
# no virtual transition can be created
if len(her_indices) > 0:
# Vectorized computation of the new reward
transitions["reward"][her_indices, 0] = 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_achieved_goal" and not "achieved_goal"
transitions["next_achieved_goal"][her_indices, 0],
# here we use the new desired goal
transitions["desired_goal"][her_indices, 0],
transitions["info"][her_indices, 0],
)
# concatenate observation with (desired) goal
observations = self._normalize_obs(transitions, maybe_vec_env)
# HACK to make normalize obs and `add()` work with the next observation
next_observations = {
"observation": transitions["next_obs"],
"achieved_goal": transitions["next_achieved_goal"],
# The desired goal for the next observation must be the same as the previous one
"desired_goal": transitions["desired_goal"],
}
next_observations = self._normalize_obs(next_observations, maybe_vec_env)
if online_sampling:
next_obs = {key: self.to_torch(next_observations[key][:, 0, :]) for key in self._observation_keys}
normalized_obs = {key: self.to_torch(observations[key][:, 0, :]) for key in self._observation_keys}
return DictReplayBufferSamples(
observations=normalized_obs,
actions=self.to_torch(transitions["action"]),
next_observations=next_obs,
dones=self.to_torch(transitions["done"]),
rewards=self.to_torch(self._normalize_reward(transitions["reward"], maybe_vec_env)),
)
else:
return observations, next_observations, transitions["action"], transitions["reward"]
[docs] def add(
self,
obs: Dict[str, np.ndarray],
next_obs: Dict[str, np.ndarray],
action: np.ndarray,
reward: np.ndarray,
done: np.ndarray,
infos: List[Dict[str, Any]],
) -> None:
if self.current_idx == 0 and self.full:
# Clear info buffer
self.info_buffer[self.pos] = deque(maxlen=self.max_episode_length)
# Remove termination signals due to timeout
if self.handle_timeout_termination:
done_ = done * (1 - np.array([info.get("TimeLimit.truncated", False) for info in infos]))
else:
done_ = done
self._buffer["observation"][self.pos][self.current_idx] = obs["observation"]
self._buffer["achieved_goal"][self.pos][self.current_idx] = obs["achieved_goal"]
self._buffer["desired_goal"][self.pos][self.current_idx] = obs["desired_goal"]
self._buffer["action"][self.pos][self.current_idx] = action
self._buffer["done"][self.pos][self.current_idx] = done_
self._buffer["reward"][self.pos][self.current_idx] = reward
self._buffer["next_obs"][self.pos][self.current_idx] = next_obs["observation"]
self._buffer["next_achieved_goal"][self.pos][self.current_idx] = next_obs["achieved_goal"]
self._buffer["next_desired_goal"][self.pos][self.current_idx] = next_obs["desired_goal"]
# When doing offline sampling
# Add real transition to normal replay buffer
if self.replay_buffer is not None:
self.replay_buffer.add(
obs,
next_obs,
action,
reward,
done,
infos,
)
self.info_buffer[self.pos].append(infos)
# update current pointer
self.current_idx += 1
self.episode_steps += 1
if done or self.episode_steps >= self.max_episode_length:
self.store_episode()
if not self.online_sampling:
# sample virtual transitions and store them in replay buffer
self._sample_her_transitions()
# clear storage for current episode
self.reset()
self.episode_steps = 0
[docs] def store_episode(self) -> None:
"""
Increment episode counter
and reset transition pointer.
"""
# add episode length to length storage
self.episode_lengths[self.pos] = self.current_idx
# update current episode pointer
# Note: in the OpenAI implementation
# when the buffer is full, the episode replaced
# is randomly chosen
self.pos += 1
if self.pos == self.max_episode_stored:
self.full = True
self.pos = 0
# reset transition pointer
self.current_idx = 0
def _sample_her_transitions(self) -> None:
"""
Sample additional goals and store new transitions in replay buffer
when using offline sampling.
"""
# Sample goals to create virtual transitions for the last episode.
observations, next_observations, actions, rewards = self._sample_offline(n_sampled_goal=self.n_sampled_goal)
# Store virtual transitions in the replay buffer, if available
if len(observations) > 0:
for i in range(len(observations["observation"])):
self.replay_buffer.add(
{key: obs[i] for key, obs in observations.items()},
{key: next_obs[i] for key, next_obs in next_observations.items()},
actions[i],
rewards[i],
# We consider the transition as non-terminal
done=[False],
infos=[{}],
)
@property
def n_episodes_stored(self) -> int:
if self.full:
return self.max_episode_stored
return self.pos
[docs] def size(self) -> int:
"""
:return: The current number of transitions in the buffer.
"""
return int(np.sum(self.episode_lengths))
[docs] def reset(self) -> None:
"""
Reset the buffer.
"""
self.pos = 0
self.current_idx = 0
self.full = False
self.episode_lengths = np.zeros(self.max_episode_stored, dtype=np.int64)
[docs] def truncate_last_trajectory(self) -> None:
"""
Only for online sampling, called when loading the replay buffer.
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
current_idx = self.current_idx
# truncate interrupted episode
if current_idx > 0:
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."
)
# get current episode and transition index
pos = self.pos
# set episode length for current episode
self.episode_lengths[pos] = current_idx
# set done = True for current episode
# current_idx was already incremented
self._buffer["done"][pos][current_idx - 1] = np.array([True], dtype=np.float32)
# reset current transition index
self.current_idx = 0
# increment episode counter
self.pos = (self.pos + 1) % self.max_episode_stored
# update "full" indicator
self.full = self.full or self.pos == 0