465 lines
18 KiB
Python
465 lines
18 KiB
Python
import torch
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import numpy as np
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from typing import Any, Tuple, Union, Optional
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from tianshou.data import Batch, SegmentTree, to_numpy
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from tianshou.data.batch import _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. The current implementation
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of Tianshou typically use 7 reserved keys in :class:`~tianshou.data.Batch`:
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* ``obs`` the observation of step :math:`t` ;
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* ``act`` the action of step :math:`t` ;
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* ``rew`` the reward of step :math:`t` ;
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* ``done`` the done flag of step :math:`t` ;
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* ``obs_next`` the observation of step :math:`t+1` ;
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* ``info`` the info of step :math:`t` (in ``gym.Env``, the ``env.step()`` \
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function returns 4 arguments, and the last one is ``info``);
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* ``policy`` the data computed by policy in step :math:`t`;
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The following code snippet illustrates its usage:
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::
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>>> import pickle, 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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>>> 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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>>> # but there are only three valid items, so len(buf) == 3.
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>>> len(buf)
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3
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>>> pickle.dump(buf, open('buf.pkl', 'wb')) # save to file "buf.pkl"
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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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>>> len(buf)
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13
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>>> buf = pickle.load(open('buf.pkl', 'rb')) # load from "buf.pkl"
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>>> len(buf)
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3
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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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:param int size: the size of replay buffer.
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:param int stack_num: the frame-stack sampling argument, should be greater
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than or equal to 1, defaults to 1 (no stacking).
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:param bool ignore_obs_next: whether to store obs_next, defaults to
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``False``.
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:param bool sample_avail: the parameter indicating sampling only available
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index when using frame-stack sampling method, defaults to ``False``.
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This feature is not supported in Prioritized Replay Buffer currently.
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"""
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def __init__(self, size: int, stack_num: int = 1,
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ignore_obs_next: bool = False,
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sample_avail: bool = False, **kwargs) -> None:
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super().__init__()
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self._maxsize = size
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self._indices = np.arange(size)
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self._stack = None
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self.stack_num = stack_num
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self._avail = sample_avail and stack_num > 1
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self._avail_index = []
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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) -> Any:
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"""Return self.key"""
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try:
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return self._meta[key]
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except KeyError as e:
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raise AttributeError from e
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def __setstate__(self, state):
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"""Unpickling interface. We need it because pickling buffer does not
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work out-of-the-box (``buffer.__getattr__`` is customized).
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"""
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self.__dict__.update(state)
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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 value.shape[1:] != inst.shape:
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raise ValueError(
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"Cannot add data to a buffer with different shape, with key "
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f"{name}, expect {value.shape[1:]}, given {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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@property
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def stack_num(self):
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return self._stack
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@stack_num.setter
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def stack_num(self, num):
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assert num > 0, 'stack_num should greater than 0'
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self._stack = num
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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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if len(buffer) == 0:
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return
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i = begin = buffer._index % len(buffer)
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stack_num_orig = buffer.stack_num
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buffer.stack_num = 1
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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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buffer.stack_num = stack_num_orig
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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: Union[int, 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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# maintain available index for frame-stack sampling
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if self._avail:
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# update current frame
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avail = sum(self.done[i] for i in range(
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self._index - self.stack_num + 1, self._index)) == 0
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if self._size < self.stack_num - 1:
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avail = False
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if avail and self._index not in self._avail_index:
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self._avail_index.append(self._index)
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elif not avail and self._index in self._avail_index:
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self._avail_index.remove(self._index)
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# remove the later available frame because of broken storage
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t = (self._index + self.stack_num - 1) % self._maxsize
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if t in self._avail_index:
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self._avail_index.remove(t)
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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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self._avail_index = []
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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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_all = self._avail_index if self._avail else self._size
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indice = np.random.choice(_all, batch_size)
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else:
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if self._avail:
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indice = np.array(self._avail_index)
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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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assert len(indice) > 0, 'No available indice can be sampled.'
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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_num
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if stack_num == 1: # the most often case
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if key != 'obs_next' or self._save_s_:
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val = self._meta.__dict__[key]
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try:
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return val[indice]
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except IndexError as e:
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if not (isinstance(val, Batch) and val.is_empty()):
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raise e # val != Batch()
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return Batch()
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indice = self._indices[:self._size][indice]
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done = self._meta.__dict__['done']
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if key == 'obs_next' and not self._save_s_:
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indice += 1 - 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 == 1:
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return val[indice]
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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 + 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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return stack
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except IndexError as e:
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if not (isinstance(val, Batch) and val.is_empty()):
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raise e # val != Batch()
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return Batch()
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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 larger than 1,
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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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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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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``. Therefore,
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it does not support advanced indexing, which means you cannot sample a
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batch of data out of it. It is typically used for storing data.
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.. seealso::
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Please refer to :class:`~tianshou.data.ReplayBuffer` for more detailed
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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 sample(self, batch_size: int) -> Tuple[Batch, np.ndarray]:
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raise NotImplementedError("ListReplayBuffer cannot be sampled!")
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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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"""Implementation of Prioritized Experience Replay. arXiv:1511.05952
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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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.. seealso::
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Please refer to :class:`~tianshou.data.ReplayBuffer` for more detailed
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explanation.
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"""
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def __init__(self, size: int, alpha: float, beta: float, **kwargs) -> None:
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super().__init__(size, **kwargs)
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assert alpha > 0. and beta >= 0.
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self._alpha, self._beta = alpha, beta
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self._max_prio = 1.
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self._min_prio = 1.
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# bypass the check
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self._weight = SegmentTree(size)
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self.__eps = np.finfo(np.float32).eps.item()
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def __getattr__(self, key: str) -> Union['Batch', Any]:
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"""Return self.key"""
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if key == 'weight':
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return self._weight
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return super().__getattr__(key)
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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: Union[int, 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 = None,
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**kwargs) -> None:
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"""Add a batch of data into replay buffer."""
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if weight is None:
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weight = self._max_prio
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else:
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weight = np.abs(weight)
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self._max_prio = max(self._max_prio, weight)
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self._min_prio = min(self._min_prio, weight)
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self.weight[self._index] = weight ** self._alpha
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super().add(obs, act, rew, done, obs_next, info, policy)
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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 priority probability. Return
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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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The ``weight`` in the returned Batch is the weight on loss function
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to de-bias the sampling process (some transition tuples are sampled
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more often so their losses are weighted less).
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"""
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assert self._size > 0, 'Cannot sample a buffer with 0 size!'
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if 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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scalar = np.random.rand(batch_size) * self.weight.reduce()
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indice = self.weight.get_prefix_sum_idx(scalar)
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batch = self[indice]
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# impt_weight
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# original formula: ((p_j/p_sum*N)**(-beta))/((p_min/p_sum*N)**(-beta))
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# simplified formula: (p_j/p_min)**(-beta)
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batch.weight = (batch.weight / self._min_prio) ** (-self._beta)
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return batch, indice
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def update_weight(self, indice: Union[np.ndarray],
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new_weight: Union[np.ndarray, torch.Tensor]) -> 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 want to update.
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"""
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weight = np.abs(to_numpy(new_weight)) + self.__eps
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self.weight[indice] = weight ** self._alpha
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self._max_prio = max(self._max_prio, weight.max())
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self._min_prio = min(self._min_prio, weight.min())
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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 Batch(
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obs=self.get(index, 'obs'),
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act=self.act[index],
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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'),
|
|
info=self.get(index, 'info'),
|
|
policy=self.get(index, 'policy'),
|
|
weight=self.weight[index],
|
|
)
|