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from collections.abc import Sequence
from typing import Union
import numpy as np
from numba import njit
from tianshou.data import Batch, HERReplayBuffer, PrioritizedReplayBuffer, ReplayBuffer
from tianshou.data.batch import alloc_by_keys_diff, create_value
from tianshou.data.types import RolloutBatchProtocol
class ReplayBufferManager(ReplayBuffer):
"""ReplayBufferManager contains a list of ReplayBuffer with exactly the same configuration.
These replay buffers have contiguous memory layout, and the storage space each
buffer has is a shallow copy of the topmost memory.
:param buffer_list: a list of ReplayBuffer needed to be handled.
.. seealso::
Please refer to :class:`~tianshou.data.ReplayBuffer` for other APIs' usage.
"""
def __init__(self, buffer_list: list[ReplayBuffer] | list[HERReplayBuffer]) -> None:
self.buffer_num = len(buffer_list)
self.buffers = np.array(buffer_list, dtype=object)
offset, size = [], 0
buffer_type = type(self.buffers[0])
kwargs = self.buffers[0].options
for buf in self.buffers:
assert buf._meta.is_empty()
assert isinstance(buf, buffer_type)
assert buf.options == kwargs
offset.append(size)
size += buf.maxsize
self._offset = np.array(offset)
self._extend_offset = np.array([*offset, size])
self._lengths = np.zeros_like(offset)
super().__init__(size=size, **kwargs)
self._compile()
self._meta: RolloutBatchProtocol
def _compile(self) -> None:
lens = last = index = np.array([0])
offset = np.array([0, 1])
done = np.array([False, False])
_prev_index(index, offset, done, last, lens)
_next_index(index, offset, done, last, lens)
def __len__(self) -> int:
return int(self._lengths.sum())
def reset(self, keep_statistics: bool = False) -> None:
self.last_index = self._offset.copy()
self._lengths = np.zeros_like(self._offset)
for buf in self.buffers:
buf.reset(keep_statistics=keep_statistics)
def _set_batch_for_children(self) -> None:
for offset, buf in zip(self._offset, self.buffers, strict=True):
buf.set_batch(self._meta[offset : offset + buf.maxsize])
def set_batch(self, batch: RolloutBatchProtocol) -> None:
super().set_batch(batch)
self._set_batch_for_children()
def unfinished_index(self) -> np.ndarray:
return np.concatenate(
[
buf.unfinished_index() + offset
for offset, buf in zip(self._offset, self.buffers, strict=True)
],
)
def prev(self, index: int | np.ndarray) -> np.ndarray:
if isinstance(index, list | np.ndarray):
return _prev_index(
np.asarray(index),
self._extend_offset,
self.done,
self.last_index,
self._lengths,
)
return _prev_index(
np.array([index]),
self._extend_offset,
self.done,
self.last_index,
self._lengths,
)[0]
def next(self, index: int | np.ndarray) -> np.ndarray:
if isinstance(index, list | np.ndarray):
return _next_index(
np.asarray(index),
self._extend_offset,
self.done,
self.last_index,
self._lengths,
)
return _next_index(
np.array([index]),
self._extend_offset,
self.done,
self.last_index,
self._lengths,
)[0]
def update(self, buffer: ReplayBuffer) -> np.ndarray:
"""The ReplayBufferManager cannot be updated by any buffer."""
raise NotImplementedError
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 ReplayBufferManager.
Each of the data's length (first dimension) must equal to the length of
buffer_ids. By default buffer_ids is [0, 1, ..., buffer_num - 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 set(self._reserved_keys).intersection(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())
if self._save_only_last_obs:
batch.obs = 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]
# get index
if buffer_ids is None:
buffer_ids = np.arange(self.buffer_num)
ptrs, ep_lens, ep_rews, ep_idxs = [], [], [], []
for batch_idx, buffer_id in enumerate(buffer_ids):
ptr, ep_rew, ep_len, ep_idx = self.buffers[buffer_id]._add_index(
batch.rew[batch_idx],
batch.done[batch_idx],
)
ptrs.append(ptr + self._offset[buffer_id])
ep_lens.append(ep_len)
ep_rews.append(ep_rew)
ep_idxs.append(ep_idx + self._offset[buffer_id])
self.last_index[buffer_id] = ptr + self._offset[buffer_id]
self._lengths[buffer_id] = len(self.buffers[buffer_id])
ptrs = np.array(ptrs)
try:
self._meta[ptrs] = batch
except ValueError:
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=False) # type: ignore
else: # dynamic key pops up in batch
alloc_by_keys_diff(self._meta, batch, self.maxsize, False)
self._set_batch_for_children()
self._meta[ptrs] = batch
return ptrs, np.array(ep_rews), np.array(ep_lens), np.array(ep_idxs)
def sample_indices(self, batch_size: int | None) -> np.ndarray:
# TODO: simplify this code
if batch_size is not None and batch_size < 0:
# TODO: raise error instead?
return np.array([], int)
if self._sample_avail and self.stack_num > 1:
all_indices = np.concatenate(
[
buf.sample_indices(0) + offset
for offset, buf in zip(self._offset, self.buffers, strict=True)
],
)
if batch_size == 0:
return all_indices
if batch_size is None:
batch_size = len(all_indices)
return np.random.choice(all_indices, batch_size)
if batch_size == 0 or batch_size is None: # get all available indices
sample_num = np.zeros(self.buffer_num, int)
else:
buffer_idx = np.random.choice(
self.buffer_num,
batch_size,
p=self._lengths / self._lengths.sum(),
)
sample_num = np.bincount(buffer_idx, minlength=self.buffer_num)
# avoid batch_size > 0 and sample_num == 0 -> get child's all data
sample_num[sample_num == 0] = -1
return np.concatenate(
[
buf.sample_indices(int(bsz)) + offset
for offset, buf, bsz in zip(self._offset, self.buffers, sample_num, strict=True)
],
)
class PrioritizedReplayBufferManager(PrioritizedReplayBuffer, ReplayBufferManager):
"""PrioritizedReplayBufferManager contains a list of PrioritizedReplayBuffer with exactly the same configuration.
These replay buffers have contiguous memory layout, and the storage space each
buffer has is a shallow copy of the topmost memory.
:param buffer_list: a list of PrioritizedReplayBuffer needed to be handled.
.. seealso::
Please refer to :class:`~tianshou.data.ReplayBuffer` for other APIs' usage.
"""
def __init__(self, buffer_list: Sequence[PrioritizedReplayBuffer]) -> None:
ReplayBufferManager.__init__(self, buffer_list) # type: ignore
kwargs = buffer_list[0].options
for buf in buffer_list:
del buf.weight
PrioritizedReplayBuffer.__init__(self, self.maxsize, **kwargs)
class HERReplayBufferManager(ReplayBufferManager):
"""HERReplayBufferManager contains a list of HERReplayBuffer with exactly the same configuration.
These replay buffers have contiguous memory layout, and the storage space each
buffer has is a shallow copy of the topmost memory.
:param buffer_list: a list of HERReplayBuffer needed to be handled.
.. seealso::
Please refer to :class:`~tianshou.data.ReplayBuffer` for other APIs' usage.
"""
def __init__(self, buffer_list: list[HERReplayBuffer]) -> None:
super().__init__(buffer_list)
def _restore_cache(self) -> None:
for buf in self.buffers:
buf._restore_cache()
def save_hdf5(self, path: str, compression: str | None = None) -> None:
self._restore_cache()
return super().save_hdf5(path, compression)
def set_batch(self, batch: RolloutBatchProtocol) -> None:
self._restore_cache()
return super().set_batch(batch)
def update(self, buffer: Union["HERReplayBuffer", "ReplayBuffer"]) -> np.ndarray:
self._restore_cache()
return super().update(buffer)
def add(
self,
batch: RolloutBatchProtocol,
buffer_ids: np.ndarray | list[int] | None = None,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
self._restore_cache()
return super().add(batch, buffer_ids)
@njit
def _prev_index(
index: np.ndarray,
offset: np.ndarray,
done: np.ndarray,
last_index: np.ndarray,
lengths: np.ndarray,
) -> np.ndarray:
index = index % offset[-1]
prev_index = np.zeros_like(index)
# disable B905 until strict=True in zip is implemented in numba
# https://github.com/numba/numba/issues/8943
for start, end, cur_len, last in zip( # noqa: B905
offset[:-1],
offset[1:],
lengths,
last_index,
):
mask = (start <= index) & (index < end)
correct_cur_len = max(1, cur_len)
if np.sum(mask) > 0:
subind = index[mask]
subind = (subind - start - 1) % correct_cur_len
end_flag = done[subind + start] | (subind + start == last)
prev_index[mask] = (subind + end_flag) % correct_cur_len + start
return prev_index
@njit
def _next_index(
index: np.ndarray,
offset: np.ndarray,
done: np.ndarray,
last_index: np.ndarray,
lengths: np.ndarray,
) -> np.ndarray:
index = index % offset[-1]
next_index = np.zeros_like(index)
# disable B905 until strict=True in zip is implemented in numba
# https://github.com/numba/numba/issues/8943
for start, end, cur_len, last in zip( # noqa: B905
offset[:-1],
offset[1:],
lengths,
last_index,
):
mask = (start <= index) & (index < end)
correct_cur_len = max(1, cur_len)
if np.sum(mask) > 0:
subind = index[mask]
end_flag = done[subind] | (subind == last)
next_index[mask] = (subind - start + 1 - end_flag) % correct_cur_len + start
return next_index
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