fc251ab0b8
* add makefile * bump version * add isort and yapf * update contributing.md * update PR template * spelling check
85 lines
3.5 KiB
Python
85 lines
3.5 KiB
Python
from typing import List, Optional, Tuple, Union
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import numpy as np
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from tianshou.data import Batch, ReplayBuffer, ReplayBufferManager
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class CachedReplayBuffer(ReplayBufferManager):
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"""CachedReplayBuffer contains a given main buffer and n cached buffers, \
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``cached_buffer_num * ReplayBuffer(size=max_episode_length)``.
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The memory layout is: ``| main_buffer | cached_buffers[0] | cached_buffers[1] | ...
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| cached_buffers[cached_buffer_num - 1] |``.
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The data is first stored in cached buffers. When an episode is terminated, the data
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will move to the main buffer and the corresponding cached buffer will be reset.
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:param ReplayBuffer main_buffer: the main buffer whose ``.update()`` function
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behaves normally.
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:param int cached_buffer_num: number of ReplayBuffer needs to be created for cached
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buffer.
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:param int max_episode_length: the maximum length of one episode, used in each
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cached buffer's maxsize.
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.. seealso::
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Please refer to :class:`~tianshou.data.ReplayBuffer` for other APIs' usage.
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"""
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def __init__(
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self,
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main_buffer: ReplayBuffer,
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cached_buffer_num: int,
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max_episode_length: int,
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) -> None:
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assert cached_buffer_num > 0 and max_episode_length > 0
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assert type(main_buffer) == ReplayBuffer
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kwargs = main_buffer.options
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buffers = [main_buffer] + [
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ReplayBuffer(max_episode_length, **kwargs)
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for _ in range(cached_buffer_num)
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]
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super().__init__(buffer_list=buffers)
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self.main_buffer = self.buffers[0]
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self.cached_buffers = self.buffers[1:]
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self.cached_buffer_num = cached_buffer_num
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def add(
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self,
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batch: Batch,
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buffer_ids: Optional[Union[np.ndarray, List[int]]] = None
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) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
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"""Add a batch of data into CachedReplayBuffer.
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Each of the data's length (first dimension) must equal to the length of
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buffer_ids. By default the buffer_ids is [0, 1, ..., cached_buffer_num - 1].
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Return (current_index, episode_reward, episode_length, episode_start_index)
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with each of the shape (len(buffer_ids), ...), where (current_index[i],
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episode_reward[i], episode_length[i], episode_start_index[i]) refers to the
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cached_buffer_ids[i]th cached buffer's corresponding episode result.
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"""
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if buffer_ids is None:
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buf_arr = np.arange(1, 1 + self.cached_buffer_num)
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else: # make sure it is np.ndarray
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buf_arr = np.asarray(buffer_ids) + 1
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ptr, ep_rew, ep_len, ep_idx = super().add(batch, buffer_ids=buf_arr)
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# find the terminated episode, move data from cached buf to main buf
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updated_ptr, updated_ep_idx = [], []
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done = batch.done.astype(bool)
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for buffer_idx in buf_arr[done]:
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index = self.main_buffer.update(self.buffers[buffer_idx])
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if len(index) == 0: # unsuccessful move, replace with -1
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index = [-1]
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updated_ep_idx.append(index[0])
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updated_ptr.append(index[-1])
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self.buffers[buffer_idx].reset()
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self._lengths[0] = len(self.main_buffer)
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self._lengths[buffer_idx] = 0
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self.last_index[0] = index[-1]
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self.last_index[buffer_idx] = self._offset[buffer_idx]
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ptr[done] = updated_ptr
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ep_idx[done] = updated_ep_idx
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return ptr, ep_rew, ep_len, ep_idx
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