class ReplayBuffer:
""":class:`~tianshou.data.ReplayBuffer` stores data generated from
interaction between the policy and environment. The current implementation
of Tianshou typically use 7 reserved keys in :class:`~tianshou.data.Batch`:
* ``obs`` the observation of step :math:`t` ;
* ``act`` the action of step :math:`t` ;
* ``rew`` the reward of step :math:`t` ;
* ``done`` the done flag of step :math:`t` ;
* ``obs_next`` the observation of step :math:`t+1` ;
* ``info`` the info of step :math:`t` (in ``gym.Env``, the ``env.step()`` \
function returns 4 arguments, and the last one is ``info``);
* ``policy`` the data computed by policy in step :math:`t`;
The following code snippet illustrates its usage:
::
>>> import pickle, 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={})
>>> 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.])
>>> # but there are only three valid items, so len(buf) == 3.
>>> len(buf)
3
>>> pickle.dump(buf, open('buf.pkl', 'wb')) # save to file "buf.pkl"
>>> 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])
>>> len(buf)
13
>>> buf = pickle.load(open('buf.pkl', 'rb')) # load from "buf.pkl"
>>> len(buf)
3
: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 or equal to 1, defaults to 1 (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: int = 1,
ignore_obs_next: bool = False,
sample_avail: bool = False,
**kwargs) -> None:
super().__init__()
self._maxsize = size
self._indices = np.arange(size)
self._stack = None
self.stack_num = stack_num
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()
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) -> Any:
"""Return self.key"""
try:
return self._meta[key]
except KeyError as e:
raise AttributeError from e
def __setstate__(self, state):
"""Unpickling interface. We need it because pickling buffer does not
work out-of-the-box (``buffer.__getattr__`` is customized).
"""
self.__dict__.update(state)
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, with key "
f"{name}, expect {value.shape[1:]}, given {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
@property
def stack_num(self):
return self._stack
@stack_num.setter
def stack_num(self, num):
assert num > 0, 'stack_num should greater than 0'
self._stack = num
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)
stack_num_orig = buffer.stack_num
buffer.stack_num = 1
while True:
self.add(**buffer[i])
i = (i + 1) % len(buffer)
if i == begin:
break
buffer.stack_num = stack_num_orig
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_num +
1, self._index)) == 0
if self._size < self.stack_num - 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_num - 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
def reset(self) -> None:
"""Clear all the data in replay buffer."""
self._index = 0
self._size = 0
self._avail_index = []
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
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_num
if stack_num == 1: # the most often case
if key != 'obs_next' or self._save_s_:
val = self._meta.__dict__[key]
try:
return val[indice]
except IndexError as e:
if not (isinstance(val, Batch) and val.is_empty()):
raise e # val != Batch()
return Batch()
indice = self._indices[:self._size][indice]
done = self._meta.__dict__['done']
if key == 'obs_next' and not self._save_s_:
indice += 1 - done[indice].astype(np.int)
indice[indice == self._size] = 0
key = 'obs'
val = self._meta.__dict__[key]
try:
if stack_num == 1:
return val[indice]
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 +
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)
return stack
except IndexError as e:
if not (isinstance(val, Batch) and val.is_empty()):
raise e # val != Batch()
return Batch()
def __getitem__(self, index: Union[slice, int, np.integer,
np.ndarray]) -> Batch:
"""Return a data batch: self[index]. If stack_num is larger than 1,
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'),
)
