from typing import Any, Self, TypeVar, cast
import h5py
import numpy as np
from tianshou.data import Batch
from tianshou.data.batch import alloc_by_keys_diff, create_value
from tianshou.data.types import RolloutBatchProtocol
from tianshou.data.utils.converter import from_hdf5, to_hdf5
TBuffer = TypeVar("TBuffer", bound="ReplayBuffer")
[docs]
class ReplayBuffer:
""":class:`~tianshou.data.ReplayBuffer` stores data generated from interaction between the policy and environment.
ReplayBuffer can be considered as a specialized form (or management) of Batch. It
stores all the data in a batch with circular-queue style.
For the example usage of ReplayBuffer, please check out Section Buffer in
:doc:`/01_tutorials/01_concepts`.
:param size: the maximum size of replay buffer.
:param stack_num: the frame-stack sampling argument, should be greater than or
equal to 1. Default to 1 (no stacking).
:param ignore_obs_next: whether to not store obs_next. Default to False.
:param save_only_last_obs: only save the last obs/obs_next when it has a shape
of (timestep, ...) because of temporal stacking. Default to False.
:param sample_avail: the parameter indicating sampling only available index
when using frame-stack sampling method. Default to False.
"""
_reserved_keys = (
"obs",
"act",
"rew",
"terminated",
"truncated",
"done",
"obs_next",
"info",
"policy",
)
_input_keys = (
"obs",
"act",
"rew",
"terminated",
"truncated",
"obs_next",
"info",
"policy",
)
def __init__(
self,
size: int,
stack_num: int = 1,
ignore_obs_next: bool = False,
save_only_last_obs: bool = False,
sample_avail: bool = False,
**kwargs: Any, # otherwise PrioritizedVectorReplayBuffer will cause TypeError
) -> None:
self.options: dict[str, Any] = {
"stack_num": stack_num,
"ignore_obs_next": ignore_obs_next,
"save_only_last_obs": save_only_last_obs,
"sample_avail": sample_avail,
}
super().__init__()
self.maxsize = int(size)
assert stack_num > 0, "stack_num should be greater than 0"
self.stack_num = stack_num
self._indices = np.arange(size)
self._save_obs_next = not ignore_obs_next
self._save_only_last_obs = save_only_last_obs
self._sample_avail = sample_avail
self._meta = cast(RolloutBatchProtocol, Batch())
self._ep_rew: float | np.ndarray
self.reset()
def __len__(self) -> int:
"""Return len(self)."""
return self._size
def __repr__(self) -> str:
"""Return str(self)."""
return self.__class__.__name__ + self._meta.__repr__()[5:]
def __getattr__(self, key: str) -> Any:
"""Return self.key."""
try:
return self._meta[key]
except KeyError as exception:
raise AttributeError from exception
def __setstate__(self, state: dict[str, Any]) -> None:
"""Unpickling interface.
We need it because pickling buffer does not work out-of-the-box
("buffer.__getattr__" is customized).
"""
self.__dict__.update(state)
def __setattr__(self, key: str, value: Any) -> None:
"""Set self.key = value."""
assert key not in self._reserved_keys, f"key '{key}' is reserved and cannot be assigned"
super().__setattr__(key, value)
[docs]
def save_hdf5(self, path: str, compression: str | None = None) -> None:
"""Save replay buffer to HDF5 file."""
with h5py.File(path, "w") as f:
to_hdf5(self.__dict__, f, compression=compression)
[docs]
@classmethod
def load_hdf5(cls, path: str, device: str | None = None) -> Self:
"""Load replay buffer from HDF5 file."""
with h5py.File(path, "r") as f:
buf = cls.__new__(cls)
buf.__setstate__(from_hdf5(f, device=device)) # type: ignore
return buf
[docs]
@classmethod
def from_data(
cls,
obs: h5py.Dataset,
act: h5py.Dataset,
rew: h5py.Dataset,
terminated: h5py.Dataset,
truncated: h5py.Dataset,
done: h5py.Dataset,
obs_next: h5py.Dataset,
) -> Self:
size = len(obs)
assert all(
len(dset) == size for dset in [obs, act, rew, terminated, truncated, done, obs_next]
), "Lengths of all hdf5 datasets need to be equal."
buf = cls(size)
if size == 0:
return buf
batch = Batch(
obs=obs,
act=act,
rew=rew,
terminated=terminated,
truncated=truncated,
done=done,
obs_next=obs_next,
)
batch = cast(RolloutBatchProtocol, batch)
buf.set_batch(batch)
buf._size = size
return buf
[docs]
def reset(self, keep_statistics: bool = False) -> None:
"""Clear all the data in replay buffer and episode statistics."""
self.last_index = np.array([0])
self._index = self._size = 0
if not keep_statistics:
self._ep_rew, self._ep_len, self._ep_idx = 0.0, 0, 0
[docs]
def set_batch(self, batch: RolloutBatchProtocol) -> None:
"""Manually choose the batch you want the ReplayBuffer to manage."""
assert len(batch) == self.maxsize and set(batch.keys()).issubset(
self._reserved_keys,
), "Input batch doesn't meet ReplayBuffer's data form requirement."
self._meta = batch
[docs]
def unfinished_index(self) -> np.ndarray:
"""Return the index of unfinished episode."""
last = (self._index - 1) % self._size if self._size else 0
return np.array([last] if not self.done[last] and self._size else [], int)
[docs]
def prev(self, index: int | np.ndarray) -> np.ndarray:
"""Return the index of previous transition.
The index won't be modified if it is the beginning of an episode.
"""
index = (index - 1) % self._size
end_flag = self.done[index] | (index == self.last_index[0])
return (index + end_flag) % self._size
[docs]
def next(self, index: int | np.ndarray) -> np.ndarray:
"""Return the index of next transition.
The index won't be modified if it is the end of an episode.
"""
end_flag = self.done[index] | (index == self.last_index[0])
return (index + (1 - end_flag)) % self._size
[docs]
def update(self, buffer: "ReplayBuffer") -> np.ndarray:
"""Move the data from the given buffer to current buffer.
Return the updated indices. If update fails, return an empty array.
"""
if len(buffer) == 0 or self.maxsize == 0:
return np.array([], int)
stack_num, buffer.stack_num = buffer.stack_num, 1
from_indices = buffer.sample_indices(0) # get all available indices
buffer.stack_num = stack_num
if len(from_indices) == 0:
return np.array([], int)
to_indices = []
for _ in range(len(from_indices)):
to_indices.append(self._index)
self.last_index[0] = self._index
self._index = (self._index + 1) % self.maxsize
self._size = min(self._size + 1, self.maxsize)
to_indices = np.array(to_indices)
if self._meta.is_empty():
self._meta = create_value(buffer._meta, self.maxsize, stack=False) # type: ignore
self._meta[to_indices] = buffer._meta[from_indices]
return to_indices
def _add_index(
self,
rew: float | np.ndarray,
done: bool,
) -> tuple[int, float | np.ndarray, int, int]:
"""Maintain the buffer's state after adding one data batch.
Return (index_to_be_modified, episode_reward, episode_length,
episode_start_index).
"""
self.last_index[0] = ptr = self._index
self._size = min(self._size + 1, self.maxsize)
self._index = (self._index + 1) % self.maxsize
self._ep_rew += rew
self._ep_len += 1
if done:
result = ptr, self._ep_rew, self._ep_len, self._ep_idx
self._ep_rew, self._ep_len, self._ep_idx = 0.0, 0, self._index
return result
return ptr, self._ep_rew * 0.0, 0, self._ep_idx
[docs]
def add(
self,
batch: RolloutBatchProtocol,
buffer_ids: np.ndarray | list[int] | None = None,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Add a batch of data into replay buffer.
:param batch: the input data batch. "obs", "act", "rew",
"terminated", "truncated" are required keys.
:param buffer_ids: to make consistent with other buffer's add function; if it
is not None, we assume the input batch's first dimension is always 1.
Return (current_index, episode_reward, episode_length, episode_start_index). If
the episode is not finished, the return value of episode_length and
episode_reward is 0.
"""
# preprocess batch
new_batch = Batch()
for key in batch.get_keys():
new_batch.__dict__[key] = batch[key]
batch = new_batch
batch.__dict__["done"] = np.logical_or(batch.terminated, batch.truncated)
assert {"obs", "act", "rew", "terminated", "truncated", "done"}.issubset(
batch.get_keys(),
) # important to do after preprocess batch
stacked_batch = buffer_ids is not None
if stacked_batch:
assert len(batch) == 1
if self._save_only_last_obs:
batch.obs = batch.obs[:, -1] if stacked_batch else batch.obs[-1]
if not self._save_obs_next:
batch.pop("obs_next", None)
elif self._save_only_last_obs:
batch.obs_next = batch.obs_next[:, -1] if stacked_batch else batch.obs_next[-1]
# get ptr
if stacked_batch:
rew, done = batch.rew[0], batch.done[0]
else:
rew, done = batch.rew, batch.done
ptr, ep_rew, ep_len, ep_idx = (np.array([x]) for x in self._add_index(rew, done))
try:
self._meta[ptr] = batch
except ValueError:
stack = not stacked_batch
batch.rew = batch.rew.astype(float)
batch.done = batch.done.astype(bool)
batch.terminated = batch.terminated.astype(bool)
batch.truncated = batch.truncated.astype(bool)
if self._meta.is_empty():
self._meta = create_value(batch, self.maxsize, stack) # type: ignore
else: # dynamic key pops up in batch
alloc_by_keys_diff(self._meta, batch, self.maxsize, stack)
self._meta[ptr] = batch
return ptr, ep_rew, ep_len, ep_idx
[docs]
def sample_indices(self, batch_size: int | None) -> np.ndarray:
"""Get a random sample of index with size = batch_size.
Return all available indices in the buffer if batch_size is 0; return an empty
numpy array if batch_size < 0 or no available index can be sampled.
:param batch_size: the number of indices to be sampled. If None, it will be set
to the length of the buffer (i.e. return all available indices in a
random order).
"""
if batch_size is None:
batch_size = len(self)
if self.stack_num == 1 or not self._sample_avail: # most often case
if batch_size > 0:
return np.random.choice(self._size, batch_size)
# TODO: is this behavior really desired?
if batch_size == 0: # construct current available indices
return np.concatenate([np.arange(self._index, self._size), np.arange(self._index)])
return np.array([], int)
# TODO: raise error on negative batch_size instead?
if batch_size < 0:
return np.array([], int)
# TODO: simplify this code - shouldn't have such a large if-else
# with many returns for handling different stack nums.
# It is also not clear whether this is really necessary - frame stacking usually is handled
# by environment wrappers (e.g. FrameStack) and not by the replay buffer.
all_indices = prev_indices = np.concatenate(
[np.arange(self._index, self._size), np.arange(self._index)],
)
for _ in range(self.stack_num - 2):
prev_indices = self.prev(prev_indices)
all_indices = all_indices[prev_indices != self.prev(prev_indices)]
if batch_size > 0:
return np.random.choice(all_indices, batch_size)
return all_indices
[docs]
def sample(self, batch_size: int | None) -> tuple[RolloutBatchProtocol, np.ndarray]:
"""Get a random sample from buffer with size = batch_size.
Return all the data in the buffer if batch_size is 0.
:return: Sample data and its corresponding index inside the buffer.
"""
indices = self.sample_indices(batch_size)
return self[indices], indices
[docs]
def get(
self,
index: int | list[int] | np.ndarray,
key: str,
default_value: Any = None,
stack_num: int | None = None,
) -> Batch | np.ndarray:
"""Return the stacked result.
E.g., if you set ``key = "obs", stack_num = 4, index = t``, it returns the
stacked result as ``[obs[t-3], obs[t-2], obs[t-1], obs[t]]``.
:param index: the index for getting stacked data.
:param str key: the key to get, should be one of the reserved_keys.
:param default_value: if the given key's data is not found and default_value is
set, return this default_value.
:param stack_num: Default to self.stack_num.
"""
if key not in self._meta and default_value is not None:
return default_value
val = self._meta[key]
if stack_num is None:
stack_num = self.stack_num
try:
if stack_num == 1: # the most common case
return val[index]
stack = list[Any]()
indices = np.array(index) if isinstance(index, list) else index
# NOTE: stack_num > 1, so the range is not empty and indices is turned into
# np.ndarray by self.prev
for _ in range(stack_num):
stack = [val[indices], *stack]
indices = self.prev(indices)
indices = cast(np.ndarray, indices)
if isinstance(val, Batch):
return Batch.stack(stack, axis=indices.ndim)
return np.stack(stack, axis=indices.ndim)
except IndexError as exception:
if not (isinstance(val, Batch) and val.is_empty()):
raise exception # val != Batch()
return Batch()
def __getitem__(self, index: slice | int | list[int] | np.ndarray) -> RolloutBatchProtocol:
"""Return a data batch: self[index].
If stack_num is larger than 1, return the stacked obs and obs_next with shape
(batch, len, ...).
"""
if isinstance(index, slice): # change slice to np array
# buffer[:] will get all available data
indices = (
self.sample_indices(0)
if index == slice(None)
else self._indices[: len(self)][index]
)
else:
indices = index # type: ignore
# raise KeyError first instead of AttributeError,
# to support np.array([ReplayBuffer()])
obs = self.get(indices, "obs")
if self._save_obs_next:
obs_next = self.get(indices, "obs_next", Batch())
else:
obs_next = self.get(self.next(indices), "obs", Batch())
batch_dict = {
"obs": obs,
"act": self.act[indices],
"rew": self.rew[indices],
"terminated": self.terminated[indices],
"truncated": self.truncated[indices],
"done": self.done[indices],
"obs_next": obs_next,
"info": self.get(indices, "info", Batch()),
# TODO: what's the use of this key?
"policy": self.get(indices, "policy", Batch()),
}
for key in self._meta.__dict__:
if key not in self._input_keys:
batch_dict[key] = self._meta[key][indices]
return cast(RolloutBatchProtocol, Batch(batch_dict))
