a951a32487
* Enable stacking of partially matching Batch instances. * Fix list support for getitem. * Fix Batch 'size' method. * Update Batch documentation.
423 lines
16 KiB
Python
423 lines
16 KiB
Python
import numpy as np
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from typing import Any, Tuple, Union, Optional
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from .batch import Batch, _create_value
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class ReplayBuffer:
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""":class:`~tianshou.data.ReplayBuffer` stores data generated from
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interaction between the policy and environment. It stores basically 7 types
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of data, as mentioned in :class:`~tianshou.data.Batch`, based on
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``numpy.ndarray``. Here is the usage:
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::
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>>> import numpy as np
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>>> from tianshou.data import ReplayBuffer
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>>> buf = ReplayBuffer(size=20)
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>>> for i in range(3):
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... buf.add(obs=i, act=i, rew=i, done=i, obs_next=i + 1, info={})
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>>> len(buf)
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3
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>>> buf.obs
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# since we set size = 20, len(buf.obs) == 20.
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array([0., 1., 2., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
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0., 0., 0., 0.])
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>>> buf2 = ReplayBuffer(size=10)
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>>> for i in range(15):
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... buf2.add(obs=i, act=i, rew=i, done=i, obs_next=i + 1, info={})
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>>> len(buf2)
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10
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>>> buf2.obs
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# since its size = 10, it only stores the last 10 steps' result.
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array([10., 11., 12., 13., 14., 5., 6., 7., 8., 9.])
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>>> # move buf2's result into buf (meanwhile keep it chronologically)
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>>> buf.update(buf2)
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array([ 0., 1., 2., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14.,
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0., 0., 0., 0., 0., 0., 0.])
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>>> # get a random sample from buffer
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>>> # the batch_data is equal to buf[incide].
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>>> batch_data, indice = buf.sample(batch_size=4)
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>>> batch_data.obs == buf[indice].obs
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array([ True, True, True, True])
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:class:`~tianshou.data.ReplayBuffer` also supports frame_stack sampling
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(typically for RNN usage, see issue#19), ignoring storing the next
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observation (save memory in atari tasks), and multi-modal observation (see
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issue#38):
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::
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>>> buf = ReplayBuffer(size=9, stack_num=4, ignore_obs_next=True)
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>>> for i in range(16):
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... done = i % 5 == 0
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... buf.add(obs={'id': i}, act=i, rew=i, done=done,
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... obs_next={'id': i + 1})
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>>> print(buf) # you can see obs_next is not saved in buf
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ReplayBuffer(
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act: array([ 9., 10., 11., 12., 13., 14., 15., 7., 8.]),
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done: array([0., 1., 0., 0., 0., 0., 1., 0., 0.]),
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info: Batch(),
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obs: Batch(
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id: array([ 9., 10., 11., 12., 13., 14., 15., 7., 8.]),
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),
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policy: Batch(),
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rew: array([ 9., 10., 11., 12., 13., 14., 15., 7., 8.]),
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)
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>>> index = np.arange(len(buf))
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>>> print(buf.get(index, 'obs').id)
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[[ 7. 7. 8. 9.]
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[ 7. 8. 9. 10.]
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[11. 11. 11. 11.]
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[11. 11. 11. 12.]
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[11. 11. 12. 13.]
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[11. 12. 13. 14.]
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[12. 13. 14. 15.]
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[ 7. 7. 7. 7.]
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[ 7. 7. 7. 8.]]
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>>> # here is another way to get the stacked data
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>>> # (stack only for obs and obs_next)
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>>> abs(buf.get(index, 'obs')['id'] - buf[index].obs.id).sum().sum()
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0.0
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>>> # we can get obs_next through __getitem__, even if it doesn't exist
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>>> print(buf[:].obs_next.id)
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[[ 7. 8. 9. 10.]
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[ 7. 8. 9. 10.]
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[11. 11. 11. 12.]
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[11. 11. 12. 13.]
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[11. 12. 13. 14.]
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[12. 13. 14. 15.]
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[12. 13. 14. 15.]
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[ 7. 7. 7. 8.]
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[ 7. 7. 8. 9.]]
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"""
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def __init__(self, size: int, stack_num: Optional[int] = 0,
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ignore_obs_next: bool = False, **kwargs) -> None:
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super().__init__()
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self._maxsize = size
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self._stack = stack_num
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self._save_s_ = not ignore_obs_next
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self._index = 0
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self._size = 0
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self._meta = Batch()
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self.reset()
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def __len__(self) -> int:
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"""Return len(self)."""
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return self._size
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def __repr__(self) -> str:
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"""Return str(self)."""
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return self.__class__.__name__ + self._meta.__repr__()[5:]
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def __getattr__(self, key: str) -> Union['Batch', Any]:
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"""Return self.key"""
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return self._meta.__dict__[key]
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def _add_to_buffer(self, name: str, inst: Any) -> None:
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try:
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value = self._meta.__dict__[name]
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except KeyError:
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self._meta.__dict__[name] = _create_value(inst, self._maxsize)
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value = self._meta.__dict__[name]
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if isinstance(inst, np.ndarray) and \
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value.shape[1:] != inst.shape:
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raise ValueError(
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"Cannot add data to a buffer with different shape, key: "
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f"{name}, expect shape: {value.shape[1:]}"
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f", given shape: {inst.shape}.")
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try:
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value[self._index] = inst
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except KeyError:
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for key in set(inst.keys()).difference(value.__dict__.keys()):
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value.__dict__[key] = _create_value(inst[key], self._maxsize)
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value[self._index] = inst
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def update(self, buffer: 'ReplayBuffer') -> None:
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"""Move the data from the given buffer to self."""
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i = begin = buffer._index % len(buffer)
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while True:
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self.add(**buffer[i])
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i = (i + 1) % len(buffer)
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if i == begin:
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break
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def add(self,
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obs: Union[dict, Batch, np.ndarray],
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act: Union[np.ndarray, float],
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rew: float,
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done: bool,
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obs_next: Optional[Union[dict, Batch, np.ndarray]] = None,
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info: dict = {},
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policy: Optional[Union[dict, Batch]] = {},
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**kwargs) -> None:
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"""Add a batch of data into replay buffer."""
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assert isinstance(info, (dict, Batch)), \
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'You should return a dict in the last argument of env.step().'
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self._add_to_buffer('obs', obs)
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self._add_to_buffer('act', act)
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self._add_to_buffer('rew', rew)
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self._add_to_buffer('done', done)
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if self._save_s_:
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if obs_next is None:
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obs_next = Batch()
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self._add_to_buffer('obs_next', obs_next)
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self._add_to_buffer('info', info)
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self._add_to_buffer('policy', policy)
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if self._maxsize > 0:
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self._size = min(self._size + 1, self._maxsize)
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self._index = (self._index + 1) % self._maxsize
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else:
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self._size = self._index = self._index + 1
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def reset(self) -> None:
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"""Clear all the data in replay buffer."""
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self._index = 0
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self._size = 0
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def sample(self, batch_size: int) -> Tuple[Batch, np.ndarray]:
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"""Get a random sample from buffer with size equal to batch_size. \
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Return all the data in the buffer if batch_size is ``0``.
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:return: Sample data and its corresponding index inside the buffer.
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"""
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if batch_size > 0:
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indice = np.random.choice(self._size, batch_size)
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else:
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indice = np.concatenate([
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np.arange(self._index, self._size),
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np.arange(0, self._index),
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])
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return self[indice], indice
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def get(self, indice: Union[slice, int, np.integer, np.ndarray], key: str,
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stack_num: Optional[int] = None) -> Union[Batch, np.ndarray]:
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"""Return the stacked result, e.g. [s_{t-3}, s_{t-2}, s_{t-1}, s_t],
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where s is self.key, t is indice. The stack_num (here equals to 4) is
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given from buffer initialization procedure.
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"""
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if stack_num is None:
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stack_num = self._stack
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if isinstance(indice, slice):
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indice = np.arange(
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0 if indice.start is None
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else self._size - indice.start if indice.start < 0
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else indice.start,
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self._size if indice.stop is None
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else self._size - indice.stop if indice.stop < 0
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else indice.stop,
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1 if indice.step is None else indice.step)
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else:
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indice = np.array(indice, copy=True)
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# set last frame done to True
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last_index = (self._index - 1 + self._size) % self._size
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last_done, self.done[last_index] = self.done[last_index], True
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if key == 'obs_next' and (not self._save_s_ or self.obs_next is None):
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indice += 1 - self.done[indice].astype(np.int)
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indice[indice == self._size] = 0
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key = 'obs'
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val = self._meta.__dict__[key]
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try:
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if stack_num > 0:
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stack = []
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for _ in range(stack_num):
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stack = [val[indice]] + stack
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pre_indice = np.asarray(indice - 1)
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pre_indice[pre_indice == -1] = self._size - 1
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indice = np.asarray(
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pre_indice + self.done[pre_indice].astype(np.int))
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indice[indice == self._size] = 0
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if isinstance(val, Batch):
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stack = Batch.stack(stack, axis=indice.ndim)
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else:
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stack = np.stack(stack, axis=indice.ndim)
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else:
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stack = val[indice]
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except TypeError:
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stack = Batch()
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self.done[last_index] = last_done
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return stack
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def __getitem__(self, index: Union[
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slice, int, np.integer, np.ndarray]) -> Batch:
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"""Return a data batch: self[index]. If stack_num is set to be > 0,
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return the stacked obs and obs_next with shape [batch, len, ...].
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"""
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return Batch(
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obs=self.get(index, 'obs'),
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act=self.act[index],
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# act_=self.get(index, 'act'), # stacked action, for RNN
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rew=self.rew[index],
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done=self.done[index],
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obs_next=self.get(index, 'obs_next'),
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info=self.get(index, 'info', stack_num=0),
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policy=self.get(index, 'policy')
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)
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class ListReplayBuffer(ReplayBuffer):
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"""The function of :class:`~tianshou.data.ListReplayBuffer` is almost the
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same as :class:`~tianshou.data.ReplayBuffer`. The only difference is that
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:class:`~tianshou.data.ListReplayBuffer` is based on ``list``.
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.. seealso::
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Please refer to :class:`~tianshou.data.ReplayBuffer` for more
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detailed explanation.
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"""
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def __init__(self, **kwargs) -> None:
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super().__init__(size=0, ignore_obs_next=False, **kwargs)
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def _add_to_buffer(
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self, name: str,
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inst: Union[dict, Batch, np.ndarray, float, int, bool]) -> None:
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if inst is None:
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return
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if self._meta.__dict__.get(name, None) is None:
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self._meta.__dict__[name] = []
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self._meta.__dict__[name].append(inst)
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def reset(self) -> None:
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self._index = self._size = 0
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for k in list(self._meta.__dict__.keys()):
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if isinstance(self._meta.__dict__[k], list):
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self._meta.__dict__[k] = []
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class PrioritizedReplayBuffer(ReplayBuffer):
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"""Prioritized replay buffer implementation.
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:param float alpha: the prioritization exponent.
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:param float beta: the importance sample soft coefficient.
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:param str mode: defaults to ``weight``.
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:param bool replace: whether to sample with replacement
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.. seealso::
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Please refer to :class:`~tianshou.data.ReplayBuffer` for more
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detailed explanation.
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"""
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def __init__(self, size: int, alpha: float, beta: float,
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mode: str = 'weight',
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replace: bool = False, **kwargs) -> None:
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if mode != 'weight':
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raise NotImplementedError
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super().__init__(size, **kwargs)
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self._alpha = alpha
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self._beta = beta
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self._weight_sum = 0.0
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self._amortization_freq = 50
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self._amortization_counter = 0
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self._replace = replace
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self._meta.__dict__['weight'] = np.zeros(size, dtype=np.float64)
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def add(self,
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obs: Union[dict, np.ndarray],
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act: Union[np.ndarray, float],
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rew: float,
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done: bool,
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obs_next: Optional[Union[dict, np.ndarray]] = None,
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info: dict = {},
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policy: Optional[Union[dict, Batch]] = {},
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weight: float = 1.0,
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**kwargs) -> None:
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"""Add a batch of data into replay buffer."""
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# we have to sacrifice some convenience for speed
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self._weight_sum += np.abs(weight) ** self._alpha - \
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self._meta.__dict__['weight'][self._index]
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self._add_to_buffer('weight', np.abs(weight) ** self._alpha)
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super().add(obs, act, rew, done, obs_next, info, policy)
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self._check_weight_sum()
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@property
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def replace(self):
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return self._replace
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@replace.setter
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def replace(self, v: bool):
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self._replace = v
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def sample(self, batch_size: int,
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importance_sample: bool = True
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) -> Tuple[Batch, np.ndarray]:
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"""Get a random sample from buffer with priority probability. \
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Return all the data in the buffer if batch_size is ``0``.
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:return: Sample data and its corresponding index inside the buffer.
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"""
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if batch_size > 0 and batch_size <= self._size:
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# Multiple sampling of the same sample
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# will cause weight update conflict
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indice = np.random.choice(
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self._size, batch_size,
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p=(self.weight / self.weight.sum())[:self._size],
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replace=self._replace)
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# self._weight_sum is not work for the accuracy issue
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# p=(self.weight/self._weight_sum)[:self._size], replace=False)
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elif batch_size == 0:
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indice = np.concatenate([
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np.arange(self._index, self._size),
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np.arange(0, self._index),
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])
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else:
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# if batch_size larger than len(self),
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# it will lead to a bug in update weight
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raise ValueError(
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"batch_size should be less than len(self), \
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or set replace=False")
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batch = self[indice]
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if importance_sample:
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impt_weight = Batch(
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impt_weight=1 / np.power(
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self._size * (batch.weight / self._weight_sum),
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self._beta))
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batch.cat_(impt_weight)
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self._check_weight_sum()
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return batch, indice
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def reset(self) -> None:
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self._amortization_counter = 0
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super().reset()
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def update_weight(self, indice: Union[slice, np.ndarray],
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new_weight: np.ndarray) -> None:
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"""Update priority weight by indice in this buffer.
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:param np.ndarray indice: indice you want to update weight
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:param np.ndarray new_weight: new priority weight you wangt to update
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"""
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if self._replace:
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if isinstance(indice, slice):
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# convert slice to ndarray
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indice = np.arange(indice.stop)[indice]
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# remove the same values in indice
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indice, unique_indice = np.unique(
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indice, return_index=True)
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new_weight = new_weight[unique_indice]
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self._weight_sum += np.power(np.abs(new_weight), self._alpha).sum() \
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- self.weight[indice].sum()
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self.weight[indice] = np.power(np.abs(new_weight), self._alpha)
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def __getitem__(self, index: Union[slice, np.ndarray]) -> Batch:
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return Batch(
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obs=self.get(index, 'obs'),
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act=self.act[index],
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# act_=self.get(index, 'act'), # stacked action, for RNN
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rew=self.rew[index],
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done=self.done[index],
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obs_next=self.get(index, 'obs_next'),
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info=self.get(index, 'info'),
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weight=self.weight[index],
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policy=self.get(index, 'policy'),
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)
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def _check_weight_sum(self) -> None:
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# keep an accurate _weight_sum
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self._amortization_counter += 1
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if self._amortization_counter % self._amortization_freq == 0:
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self._weight_sum = np.sum(self.weight)
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self._amortization_counter = 0
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