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