b900fdf6f2
Closes #947 This removes all kwargs from all policy constructors. While doing that, I also improved several names and added a whole lot of TODOs. ## Functional changes: 1. Added possibility to pass None as `critic2` and `critic2_optim`. In fact, the default behavior then should cover the absolute majority of cases 2. Added a function called `clone_optimizer` as a temporary measure to support passing `critic2_optim=None` ## Breaking changes: 1. `action_space` is no longer optional. In fact, it already was non-optional, as there was a ValueError in BasePolicy.init. So now several examples were fixed to reflect that 2. `reward_normalization` removed from DDPG and children. It was never allowed to pass it as `True` there, an error would have been raised in `compute_n_step_reward`. Now I removed it from the interface 3. renamed `critic1` and similar to `critic`, in order to have uniform interfaces. Note that the `critic` in DDPG was optional for the sole reason that child classes used `critic1`. I removed this optionality (DDPG can't do anything with `critic=None`) 4. Several renamings of fields (mostly private to public, so backwards compatible) ## Additional changes: 1. Removed type and default declaration from docstring. This kind of duplication is really not necessary 2. Policy constructors are now only called using named arguments, not a fragile mixture of positional and named as before 5. Minor beautifications in typing and code 6. Generally shortened docstrings and made them uniform across all policies (hopefully) ## Comment: With these changes, several problems in tianshou's inheritance hierarchy become more apparent. I tried highlighting them for future work. --------- Co-authored-by: Dominik Jain <d.jain@appliedai.de>
83 lines
3.5 KiB
Python
83 lines
3.5 KiB
Python
import numpy as np
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from tianshou.data import ReplayBuffer, ReplayBufferManager
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from tianshou.data.types import RolloutBatchProtocol
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class CachedReplayBuffer(ReplayBufferManager):
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"""CachedReplayBuffer contains a given main buffer and n cached buffers, ``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 main_buffer: the main buffer whose ``.update()`` function
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behaves normally.
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:param cached_buffer_num: number of ReplayBuffer needs to be created for cached
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buffer.
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:param 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
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assert max_episode_length > 0
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assert isinstance(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) 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: RolloutBatchProtocol,
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buffer_ids: np.ndarray | list[int] | None = 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 = np.logical_or(batch.terminated, batch.truncated)
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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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