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e913bc81b8
* feat: add `isort` to `pre-commit` * ci: skip `__init__.py` file for `isort` * ci: make `isort` mandatory in lint pipeline * docs: add a section on Git hooks * ci: check isort diff * fix: isort from master branch * docs: add pre-commit badge * ci: update black + bandit versions * feat: add PR template * refactor: PR template * ci: remove bandit * docs: add Black badge * ci: try to remove all `|| true` statements * ci: remove lint_python job - Remove `lint_python` CI job - Move `pyupgrade` job to `pre-commit` workflow * fix: avoid messing with typing * docs: add a note on running `pre-cpmmit` manually * ci: apply `pre-commit` to the whole codebase
134 lines
4.1 KiB
Python
134 lines
4.1 KiB
Python
from collections import deque
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from typing import Optional
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import numpy as np
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from gym import ObservationWrapper
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from gym.spaces import Box
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class LazyFrames:
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r"""Ensures common frames are only stored once to optimize memory use.
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To further reduce the memory use, it is optionally to turn on lz4 to
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compress the observations.
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.. note::
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This object should only be converted to numpy array just before forward pass.
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Args:
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lz4_compress (bool): use lz4 to compress the frames internally
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"""
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__slots__ = ("frame_shape", "dtype", "shape", "lz4_compress", "_frames")
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def __init__(self, frames, lz4_compress=False):
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self.frame_shape = tuple(frames[0].shape)
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self.shape = (len(frames),) + self.frame_shape
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self.dtype = frames[0].dtype
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if lz4_compress:
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from lz4.block import compress
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frames = [compress(frame) for frame in frames]
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self._frames = frames
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self.lz4_compress = lz4_compress
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def __array__(self, dtype=None):
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arr = self[:]
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if dtype is not None:
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return arr.astype(dtype)
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return arr
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def __len__(self):
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return self.shape[0]
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def __getitem__(self, int_or_slice):
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if isinstance(int_or_slice, int):
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return self._check_decompress(self._frames[int_or_slice]) # single frame
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return np.stack(
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[self._check_decompress(f) for f in self._frames[int_or_slice]], axis=0
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)
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def __eq__(self, other):
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return self.__array__() == other
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def _check_decompress(self, frame):
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if self.lz4_compress:
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from lz4.block import decompress
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return np.frombuffer(decompress(frame), dtype=self.dtype).reshape(
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self.frame_shape
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)
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return frame
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class FrameStack(ObservationWrapper):
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r"""Observation wrapper that stacks the observations in a rolling manner.
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For example, if the number of stacks is 4, then the returned observation contains
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the most recent 4 observations. For environment 'Pendulum-v1', the original observation
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is an array with shape [3], so if we stack 4 observations, the processed observation
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has shape [4, 3].
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.. note::
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To be memory efficient, the stacked observations are wrapped by :class:`LazyFrame`.
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.. note::
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The observation space must be `Box` type. If one uses `Dict`
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as observation space, it should apply `FlattenDictWrapper` at first.
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Example::
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>>> import gym
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>>> env = gym.make('PongNoFrameskip-v0')
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>>> env = FrameStack(env, 4)
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>>> env.observation_space
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Box(4, 210, 160, 3)
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Args:
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env (Env): environment object
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num_stack (int): number of stacks
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lz4_compress (bool): use lz4 to compress the frames internally
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"""
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def __init__(self, env, num_stack, lz4_compress=False):
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super().__init__(env)
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self.num_stack = num_stack
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self.lz4_compress = lz4_compress
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self.frames = deque(maxlen=num_stack)
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low = np.repeat(self.observation_space.low[np.newaxis, ...], num_stack, axis=0)
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high = np.repeat(
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self.observation_space.high[np.newaxis, ...], num_stack, axis=0
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)
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self.observation_space = Box(
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low=low, high=high, dtype=self.observation_space.dtype
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)
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def observation(self):
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assert len(self.frames) == self.num_stack, (len(self.frames), self.num_stack)
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return LazyFrames(list(self.frames), self.lz4_compress)
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def step(self, action):
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observation, reward, done, info = self.env.step(action)
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self.frames.append(observation)
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return self.observation(), reward, done, info
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def reset(self, **kwargs):
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if kwargs.get("return_info", False):
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obs, info = self.env.reset(**kwargs)
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else:
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obs = self.env.reset(**kwargs)
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info = None # Unused
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[self.frames.append(obs) for _ in range(self.num_stack)]
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if kwargs.get("return_info", False):
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return self.observation(), info
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else:
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return self.observation()
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