Use lower-level API to reduce overhead. (#97)
* Use lower-level API to reduce overhead. * Further improvements. * Buffer _add_to_buffer improvement. * Do not use _data field to store Batch data to avoid overhead. Add back _meta field in Buffer. * Restore metadata attribute to store batch in Buffer. * Move out nested methods. * Update try/catch instead of actual check to efficiency. * Remove unsed branches for efficiency. * Use np.array over list when possible for efficiency. * Final performance improvement. * Add unit tests for Batch size method. * Add missing stack unit tests. * Enforce Buffer initialization to zero. Co-authored-by: Alexis Duburcq <alexis.duburcq@wandercraft.eu>
This commit is contained in:
Alexis DUBURCQ
GitHub
parent
5ac9f9b144
commit
70aa7bf93e
@ -39,12 +39,17 @@ def test_batch():
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'c': np.zeros(1),
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'd': Batch(e=np.array(3.0))}])
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assert len(batch2) == 1
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assert Batch().size == 0
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assert batch2.size == 1
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with pytest.raises(IndexError):
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batch2[-2]
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with pytest.raises(IndexError):
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batch2[1]
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assert batch2[0].size == 1
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with pytest.raises(TypeError):
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batch2[0][0]
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with pytest.raises(TypeError):
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len(batch2[0])
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assert isinstance(batch2[0].a.c, np.ndarray)
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assert isinstance(batch2[0].a.b, np.float64)
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assert isinstance(batch2[0].a.d.e, np.float64)
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@ -72,7 +77,7 @@ def test_batch():
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assert batch3.a.d.e[0] == 4.0
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batch3.a.d[0] = Batch(f=5.0)
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assert batch3.a.d.f[0] == 5.0
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with pytest.raises(ValueError):
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with pytest.raises(KeyError):
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batch3.a.d[0] = Batch(f=5.0, g=0.0)
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@ -112,10 +117,15 @@ def test_batch_cat_and_stack():
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b12_stack = Batch.stack((b1, b2))
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assert isinstance(b12_stack.a.d.e, np.ndarray)
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assert b12_stack.a.d.e.ndim == 2
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b3 = Batch(a=np.zeros((3, 4)))
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b4 = Batch(a=np.ones((3, 4)))
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b3 = Batch(a=np.zeros((3, 4)),
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b=torch.ones((2, 5)),
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c=Batch(d=[[1], [2]]))
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b4 = Batch(a=np.ones((3, 4)),
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b=torch.ones((2, 5)),
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c=Batch(d=[[0], [3]]))
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b34_stack = Batch.stack((b3, b4), axis=1)
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assert np.all(b34_stack.a == np.stack((b3.a, b4.a), axis=1))
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assert np.all(b34_stack.c.d == list(map(list, zip(b3.c.d, b4.c.d))))
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def test_batch_over_batch_to_torch():
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@ -27,6 +27,16 @@ def test_replaybuffer(size=10, bufsize=20):
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assert len(buf) == len(buf2)
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assert buf2[0].obs == buf[5].obs
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assert buf2[-1].obs == buf[4].obs
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b = ReplayBuffer(size=10)
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b.add(1, 1, 1, 'str', 1, {'a': 3, 'b': {'c': 5.0}})
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assert b.obs[0] == 1
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assert b.done[0] == 'str'
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assert np.all(b.obs[1:] == 0)
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assert np.all(b.done[1:] == np.array(None))
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assert b.info.a[0] == 3 and b.info.a.dtype == np.integer
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assert np.all(b.info.a[1:] == 0)
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assert b.info.b.c[0] == 5.0 and b.info.b.c.dtype == np.inexact
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assert np.all(np.isnan(b.info.b.c[1:]))
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def test_ignore_obs_next(size=10):
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@ -13,6 +13,35 @@ warnings.filterwarnings(
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"ignore", message="pickle support for Storage will be removed in 1.5.")
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def _is_batch_set(data: Any) -> bool:
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if isinstance(data, (list, tuple)):
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if len(data) > 0 and isinstance(data[0], (dict, Batch)):
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return True
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elif isinstance(data, np.ndarray):
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if isinstance(data.item(0), (dict, Batch)):
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return True
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return False
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def _valid_bounds(length: int, index: Union[
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slice, int, np.integer, np.ndarray, List[int]]) -> bool:
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if isinstance(index, (int, np.integer)):
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return -length <= index and index < length
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elif isinstance(index, (list, np.ndarray)):
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return _valid_bounds(length, np.min(index)) and \
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_valid_bounds(length, np.max(index))
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elif isinstance(index, slice):
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if index.start is not None:
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start_valid = _valid_bounds(length, index.start)
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else:
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start_valid = True
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if index.stop is not None:
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stop_valid = _valid_bounds(length, index.stop - 1)
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else:
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stop_valid = True
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return start_valid and stop_valid
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class Batch:
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"""Tianshou provides :class:`~tianshou.data.Batch` as the internal data
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structure to pass any kind of data to other methods, for example, a
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@ -75,46 +104,30 @@ class Batch:
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[11 22] [6 6]
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"""
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def __new__(cls, *args, **kwargs) -> 'Batch':
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self = super().__new__(cls)
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self.__dict__['_data'] = {}
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return self
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def __init__(self,
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batch_dict: Optional[Union[
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dict, 'Batch', Tuple[Union[dict, 'Batch']],
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List[Union[dict, 'Batch']], np.ndarray]] = None,
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**kwargs) -> None:
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def _is_batch_set(data: Any) -> bool:
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if isinstance(data, (list, tuple)):
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if len(data) > 0 and isinstance(data[0], (dict, Batch)):
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return True
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elif isinstance(data, np.ndarray):
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if isinstance(data.item(0), (dict, Batch)):
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return True
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return False
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if isinstance(batch_dict, np.ndarray) and batch_dict.ndim == 0:
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batch_dict = batch_dict[()]
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if _is_batch_set(batch_dict):
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for k, v in zip(batch_dict[0].keys(),
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zip(*[e.values() for e in batch_dict])):
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if isinstance(v[0], dict) or _is_batch_set(v[0]):
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self[k] = Batch(v)
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self.__dict__[k] = Batch(v)
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elif isinstance(v[0], (np.generic, np.ndarray)):
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self[k] = np.stack(v, axis=0)
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self.__dict__[k] = np.stack(v, axis=0)
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elif isinstance(v[0], torch.Tensor):
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self[k] = torch.stack(v, dim=0)
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self.__dict__[k] = torch.stack(v, dim=0)
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elif isinstance(v[0], Batch):
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self[k] = Batch.stack(v)
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self.__dict__[k] = Batch.stack(v)
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else:
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self[k] = np.array(v) # fall back to np.object
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self.__dict__[k] = np.array(v)
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elif isinstance(batch_dict, (dict, Batch)):
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for k, v in batch_dict.items():
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if isinstance(v, dict) or _is_batch_set(v):
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self[k] = Batch(v)
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self.__dict__[k] = Batch(v)
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else:
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self[k] = v
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self.__dict__[k] = v
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if len(kwargs) > 0:
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self.__init__(kwargs)
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@ -123,8 +136,7 @@ class Batch:
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for both efficiency and simplicity.
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"""
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state = {}
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for k in self.keys():
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v = self[k]
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for k, v in self.items():
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if isinstance(v, Batch):
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v = v.__getstate__()
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state[k] = v
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@ -140,26 +152,8 @@ class Batch:
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def __getitem__(self, index: Union[
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str, slice, int, np.integer, np.ndarray, List[int]]) -> 'Batch':
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"""Return self[index]."""
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def _valid_bounds(length: int, index: Union[
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slice, int, np.integer, np.ndarray, List[int]]) -> bool:
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if isinstance(index, (int, np.integer)):
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return -length <= index and index < length
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elif isinstance(index, (list, np.ndarray)):
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return _valid_bounds(length, np.min(index)) and \
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_valid_bounds(length, np.max(index))
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elif isinstance(index, slice):
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if index.start is not None:
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start_valid = _valid_bounds(length, index.start)
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else:
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start_valid = True
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if index.stop is not None:
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stop_valid = _valid_bounds(length, index.stop - 1)
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else:
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stop_valid = True
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return start_valid and stop_valid
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if isinstance(index, str):
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return getattr(self, index)
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return self.__dict__[index]
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if not _valid_bounds(len(self), index):
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raise IndexError(
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@ -167,61 +161,57 @@ class Batch:
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else:
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b = Batch()
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for k, v in self.items():
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if isinstance(v, Batch) and v.size == 0:
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b[k] = Batch()
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elif hasattr(v, '__len__') and (not isinstance(
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v, (np.ndarray, torch.Tensor)) or v.ndim > 0):
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if isinstance(index, (int, np.integer)) or \
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(isinstance(index, np.ndarray) and
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index.ndim == 0) or not isinstance(v, list):
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b[k] = v[index]
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else:
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b[k] = [v[i] for i in index]
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if isinstance(v, Batch) and len(v.__dict__) == 0:
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b.__dict__[k] = Batch()
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else:
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b.__dict__[k] = v[index]
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return b
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def __setitem__(self, index: Union[
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str, slice, int, np.integer, np.ndarray, List[int]],
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value: Any) -> None:
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if isinstance(index, str):
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return setattr(self, index, value)
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if value is None:
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value = Batch()
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self.__dict__[index] = value
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return
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if not isinstance(value, (dict, Batch)):
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raise TypeError("Batch does not supported value type "
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f"{type(value)} for item assignment.")
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if not set(value.keys()).issubset(self.keys()):
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raise ValueError(
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if not set(value.keys()).issubset(self.__dict__.keys()):
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raise KeyError(
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"Creating keys is not supported by item assignment.")
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for key in self.keys():
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if isinstance(self[key], Batch):
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default = Batch()
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elif isinstance(self[key], np.ndarray) and \
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self[key].dtype == np.integer:
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# Fallback for np.array of integer,
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# since neither None or nan is supported.
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default = 0
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else:
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default = None
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self[key][index] = value.get(key, default)
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for key, val in self.items():
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try:
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self.__dict__[key][index] = value[key]
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except KeyError:
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if isinstance(val, Batch):
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self.__dict__[key][index] = Batch()
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elif isinstance(val, np.ndarray) and \
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val.dtype == np.integer:
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# Fallback for np.array of integer,
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# since neither None or nan is supported.
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self.__dict__[key][index] = 0
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else:
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self.__dict__[key][index] = None
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def __iadd__(self, val: Union['Batch', Number]):
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if isinstance(val, Batch):
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for k, r, v in zip(self.keys(), self.values(), val.values()):
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for (k, r), v in zip(self.__dict__.items(),
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val.__dict__.values()):
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if r is None:
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self[k] = r
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continue
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elif isinstance(r, list):
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self[k] = [r_ + v_ for r_, v_ in zip(r, v)]
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self.__dict__[k] = [r_ + v_ for r_, v_ in zip(r, v)]
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else:
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self[k] = r + v
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self.__dict__[k] += v
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return self
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elif isinstance(val, Number):
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for k, r in zip(self.keys(), self.values()):
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for k, r in self.items():
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if r is None:
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self[k] = r
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continue
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elif isinstance(r, list):
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self[k] = [r_ + val for r_ in r]
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self.__dict__[k] = [r_ + val for r_ in r]
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else:
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self[k] = r + val
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self.__dict__[k] += val
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return self
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else:
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raise TypeError("Only addition of Batch or number is supported.")
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@ -229,37 +219,25 @@ class Batch:
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def __add__(self, val: Union['Batch', Number]):
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return copy.deepcopy(self).__iadd__(val)
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def __mul__(self, val: Number):
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def __imul__(self, val: Number):
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assert isinstance(val, Number), \
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"Only multiplication by a number is supported."
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result = self.__class__()
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for k, r in zip(self.keys(), self.values()):
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result[k] = r * val
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return result
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for k in self.__dict__.keys():
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self.__dict__[k] *= val
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return self
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def __truediv__(self, val: Number):
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def __mul__(self, val: Number):
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return copy.deepcopy(self).__imul__(val)
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def __itruediv__(self, val: Number):
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assert isinstance(val, Number), \
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"Only division by a number is supported."
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result = self.__class__()
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for k, r in zip(self.keys(), self.values()):
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result[k] = r / val
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return result
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for k in self.__dict__.keys():
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self.__dict__[k] /= val
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return self
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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 in self.__dict__.keys():
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return self.__dict__[key]
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elif key in self._data.keys():
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return self._data[key]
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raise AttributeError(key)
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def __setattr__(self, key, value):
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if key in self._data.keys():
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self._data[key] = value
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elif key in self.__dict__.keys():
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self.__dict__[key] = value
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else:
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self._data[key] = value
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def __truediv__(self, val: Number):
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return copy.deepcopy(self).__itruediv__(val)
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def __repr__(self) -> str:
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"""Return str(self)."""
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@ -278,21 +256,19 @@ class Batch:
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def keys(self) -> List[str]:
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"""Return self.keys()."""
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return self._data.keys()
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return self.__dict__.keys()
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def values(self) -> List[Any]:
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"""Return self.values()."""
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return self._data.values()
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return self.__dict__.values()
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def items(self) -> Any:
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def items(self) -> List[Tuple[str, Any]]:
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"""Return self.items()."""
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return self._data.items()
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return self.__dict__.items()
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def get(self, k: str, d: Optional[Any] = None) -> Union['Batch', Any]:
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"""Return self[k] if k in self else d. d defaults to None."""
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if k in self.keys():
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return self[k]
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return d
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return self.__dict__.get(k, d)
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def to_numpy(self) -> None:
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"""Change all torch.Tensor to numpy.ndarray. This is an in-place
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@ -300,7 +276,7 @@ class Batch:
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"""
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for k, v in self.items():
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if isinstance(v, torch.Tensor):
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self[k] = v.detach().cpu().numpy()
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self.__dict__[k] = v.detach().cpu().numpy()
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elif isinstance(v, Batch):
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v.to_numpy()
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@ -319,7 +295,7 @@ class Batch:
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v = torch.from_numpy(v).to(device)
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if dtype is not None:
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v = v.type(dtype)
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self[k] = v
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self.__dict__[k] = v
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if isinstance(v, torch.Tensor):
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if dtype is not None and v.dtype != dtype:
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must_update_tensor = True
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@ -333,7 +309,7 @@ class Batch:
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if must_update_tensor:
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if dtype is not None:
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v = v.type(dtype)
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self[k] = v.to(device)
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self.__dict__[k] = v.to(device)
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elif isinstance(v, Batch):
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v.to_torch(dtype, device)
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@ -351,16 +327,16 @@ class Batch:
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for k, v in batch.items():
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if v is None:
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continue
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if not hasattr(self, k) or self[k] is None:
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self[k] = copy.deepcopy(v)
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if not hasattr(self, k) or self.__dict__[k] is None:
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self.__dict__[k] = copy.deepcopy(v)
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elif isinstance(v, np.ndarray) and v.ndim > 0:
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self[k] = np.concatenate([self[k], v])
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self.__dict__[k] = np.concatenate([self.__dict__[k], v])
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elif isinstance(v, torch.Tensor):
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self[k] = torch.cat([self[k], v])
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self.__dict__[k] = torch.cat([self.__dict__[k], v])
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elif isinstance(v, list):
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self[k] = self[k] + copy.deepcopy(v)
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self.__dict__[k] += copy.deepcopy(v)
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elif isinstance(v, Batch):
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self[k].cat_(v)
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self.__dict__[k].cat_(v)
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else:
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s = 'No support for method "cat" with type '\
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f'{type(v)} in class Batch.'
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@ -394,11 +370,11 @@ class Batch:
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for k, v in zip(batches[0].keys(),
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zip(*[e.values() for e in batches])):
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if isinstance(v[0], (np.generic, np.ndarray, list)):
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batch[k] = np.stack(v, axis)
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batch.__dict__[k] = np.stack(v, axis)
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elif isinstance(v[0], torch.Tensor):
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batch[k] = torch.stack(v, axis)
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batch.__dict__[k] = torch.stack(v, axis)
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elif isinstance(v[0], Batch):
|
||||
batch[k] = Batch.stack(v, axis)
|
||||
batch.__dict__[k] = Batch.stack(v, axis)
|
||||
else:
|
||||
s = 'No support for method "stack" with type '\
|
||||
f'{type(v[0])} in class Batch and axis != 0.'
|
||||
@ -408,10 +384,8 @@ class Batch:
|
||||
def __len__(self) -> int:
|
||||
"""Return len(self)."""
|
||||
r = []
|
||||
for v in self.values():
|
||||
if isinstance(v, Batch) and v.size == 0:
|
||||
continue
|
||||
elif isinstance(v, list) and len(v) == 0:
|
||||
for v in self.__dict__.values():
|
||||
if isinstance(v, Batch) and len(v.__dict__) == 0:
|
||||
continue
|
||||
elif hasattr(v, '__len__') and (not isinstance(
|
||||
v, (np.ndarray, torch.Tensor)) or v.ndim > 0):
|
||||
@ -425,11 +399,11 @@ class Batch:
|
||||
@property
|
||||
def size(self) -> int:
|
||||
"""Return self.size."""
|
||||
if len(self.keys()) == 0:
|
||||
if len(self.__dict__.keys()) == 0:
|
||||
return 0
|
||||
else:
|
||||
r = []
|
||||
for v in self.values():
|
||||
for v in self.__dict__.values():
|
||||
if isinstance(v, Batch):
|
||||
r.append(v.size)
|
||||
elif hasattr(v, '__len__') and (not isinstance(
|
||||
|
||||
@ -5,7 +5,23 @@ from typing import Any, Tuple, Union, Optional
|
||||
from .batch import Batch
|
||||
|
||||
|
||||
class ReplayBuffer(Batch):
|
||||
def _create_value(inst: Any, size: int) -> Union['Batch', np.ndarray]:
|
||||
if isinstance(inst, np.ndarray):
|
||||
return np.full(shape=(size, *inst.shape),
|
||||
fill_value=None if inst.dtype == np.inexact else 0,
|
||||
dtype=inst.dtype)
|
||||
elif isinstance(inst, (dict, Batch)):
|
||||
zero_batch = Batch()
|
||||
for key, val in inst.items():
|
||||
zero_batch.__dict__[key] = _create_value(val, size)
|
||||
return zero_batch
|
||||
elif isinstance(inst, (np.generic, Number)):
|
||||
return _create_value(np.asarray(inst), size)
|
||||
else: # fall back to np.object
|
||||
return np.array([None for _ in range(size)])
|
||||
|
||||
|
||||
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
|
||||
@ -93,50 +109,46 @@ class ReplayBuffer(Batch):
|
||||
[ 7. 7. 7. 8.]
|
||||
[ 7. 7. 8. 9.]]
|
||||
"""
|
||||
|
||||
def __init__(self, size: int, stack_num: Optional[int] = 0,
|
||||
ignore_obs_next: bool = False, **kwargs) -> None:
|
||||
super().__init__()
|
||||
self.__dict__['_maxsize'] = size
|
||||
self.__dict__['_stack'] = stack_num
|
||||
self.__dict__['_save_s_'] = not ignore_obs_next
|
||||
self.__dict__['_index'] = 0
|
||||
self.__dict__['_size'] = 0
|
||||
self._maxsize = size
|
||||
self._stack = stack_num
|
||||
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 _add_to_buffer(self, name: str, inst: Any) -> None:
|
||||
def _create_value(inst: Any) -> Union['Batch', np.ndarray]:
|
||||
if isinstance(inst, np.ndarray):
|
||||
return np.zeros(
|
||||
(self._maxsize, *inst.shape), dtype=inst.dtype)
|
||||
elif isinstance(inst, (dict, Batch)):
|
||||
return Batch([Batch(inst) for _ in range(self._maxsize)])
|
||||
elif isinstance(inst, (np.generic, Number)):
|
||||
return np.zeros(
|
||||
(self._maxsize,), dtype=np.asarray(inst).dtype)
|
||||
else: # fall back to np.object
|
||||
return np.array([None for _ in range(self._maxsize)])
|
||||
def __repr__(self) -> str:
|
||||
return self.__class__.__name__ + self._meta.__repr__()[5:]
|
||||
|
||||
if inst is None:
|
||||
inst = Batch()
|
||||
if name not in self.keys():
|
||||
self[name] = _create_value(inst)
|
||||
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 \
|
||||
self[name].shape[1:] != inst.shape:
|
||||
value.shape[1:] != inst.shape:
|
||||
raise ValueError(
|
||||
"Cannot add data to a buffer with different shape, "
|
||||
f"key: {name}, expect shape: {self[name].shape[1:]}"
|
||||
"Cannot add data to a buffer with different shape, key: "
|
||||
f"{name}, expect shape: {value.shape[1:]}"
|
||||
f", given shape: {inst.shape}.")
|
||||
if isinstance(self[name], Batch):
|
||||
field_keys = self[name].keys()
|
||||
for key, val in inst.items():
|
||||
if key not in field_keys:
|
||||
self[name][key] = _create_value(val)
|
||||
self[name][self._index] = inst
|
||||
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
|
||||
|
||||
def update(self, buffer: 'ReplayBuffer') -> None:
|
||||
"""Move the data from the given buffer to self."""
|
||||
@ -148,11 +160,11 @@ class ReplayBuffer(Batch):
|
||||
break
|
||||
|
||||
def add(self,
|
||||
obs: Union[dict, np.ndarray],
|
||||
obs: Union[dict, Batch, np.ndarray],
|
||||
act: Union[np.ndarray, float],
|
||||
rew: float,
|
||||
done: bool,
|
||||
obs_next: Optional[Union[dict, np.ndarray]] = None,
|
||||
obs_next: Optional[Union[dict, Batch, np.ndarray]] = None,
|
||||
info: dict = {},
|
||||
policy: Optional[Union[dict, Batch]] = {},
|
||||
**kwargs) -> None:
|
||||
@ -164,6 +176,8 @@ class ReplayBuffer(Batch):
|
||||
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)
|
||||
@ -210,7 +224,8 @@ class ReplayBuffer(Batch):
|
||||
else self._size - indice.stop if indice.stop < 0
|
||||
else indice.stop,
|
||||
1 if indice.step is None else indice.step)
|
||||
indice = np.array(indice, copy=True)
|
||||
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
|
||||
@ -218,21 +233,9 @@ class ReplayBuffer(Batch):
|
||||
indice += 1 - self.done[indice].astype(np.int)
|
||||
indice[indice == self._size] = 0
|
||||
key = 'obs'
|
||||
if stack_num == 0:
|
||||
self.done[last_index] = last_done
|
||||
val = self[key]
|
||||
if isinstance(val, Batch) and val.size == 0:
|
||||
return val
|
||||
else:
|
||||
if isinstance(indice, (int, np.integer)) or \
|
||||
(isinstance(indice, np.ndarray) and
|
||||
indice.ndim == 0) or not isinstance(val, list):
|
||||
return val[indice]
|
||||
else:
|
||||
return [val[i] for i in indice]
|
||||
else:
|
||||
val = self[key]
|
||||
if not isinstance(val, Batch) or val.size > 0:
|
||||
val = self._meta.__dict__[key]
|
||||
try:
|
||||
if stack_num > 0:
|
||||
stack = []
|
||||
for _ in range(stack_num):
|
||||
stack = [val[indice]] + stack
|
||||
@ -241,31 +244,31 @@ class ReplayBuffer(Batch):
|
||||
indice = np.asarray(
|
||||
pre_indice + self.done[pre_indice].astype(np.int))
|
||||
indice[indice == self._size] = 0
|
||||
if isinstance(stack[0], Batch):
|
||||
if isinstance(val, Batch):
|
||||
stack = Batch.stack(stack, axis=indice.ndim)
|
||||
else:
|
||||
stack = np.stack(stack, axis=indice.ndim)
|
||||
else:
|
||||
stack = Batch()
|
||||
self.done[last_index] = last_done
|
||||
return stack
|
||||
stack = val[indice]
|
||||
except TypeError:
|
||||
stack = Batch()
|
||||
self.done[last_index] = last_done
|
||||
return stack
|
||||
|
||||
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, ...].
|
||||
"""
|
||||
if isinstance(index, str):
|
||||
return getattr(self, index)
|
||||
return Batch(
|
||||
obs=self.get(index, 'obs'),
|
||||
act=self.get(index, 'act', stack_num=0),
|
||||
act=self.act[index],
|
||||
# act_=self.get(index, 'act'), # stacked action, for RNN
|
||||
rew=self.get(index, 'rew', stack_num=0),
|
||||
done=self.get(index, 'done', stack_num=0),
|
||||
rew=self.rew[index],
|
||||
done=self.done[index],
|
||||
obs_next=self.get(index, 'obs_next'),
|
||||
info=self.get(index, 'info', stack_num=0),
|
||||
policy=self.get(index, 'policy'),
|
||||
policy=self.get(index, 'policy')
|
||||
)
|
||||
|
||||
|
||||
@ -288,15 +291,15 @@ class ListReplayBuffer(ReplayBuffer):
|
||||
inst: Union[dict, Batch, np.ndarray, float, int, bool]) -> None:
|
||||
if inst is None:
|
||||
return
|
||||
if self._data.get(name, None) is None:
|
||||
self._data[name] = []
|
||||
self._data[name].append(inst)
|
||||
if self._meta.__dict__.get(name, None) is None:
|
||||
self._meta.__dict__[name] = []
|
||||
self._meta.__dict__[name].append(inst)
|
||||
|
||||
def reset(self) -> None:
|
||||
self._index = self._size = 0
|
||||
for k in list(self._data):
|
||||
if isinstance(self._data[k], list):
|
||||
self._data[k] = []
|
||||
for k in list(self._meta.__dict__.keys()):
|
||||
if isinstance(self._meta.__dict__[k], list):
|
||||
self._meta.__dict__[k] = []
|
||||
|
||||
|
||||
class PrioritizedReplayBuffer(ReplayBuffer):
|
||||
@ -322,10 +325,10 @@ class PrioritizedReplayBuffer(ReplayBuffer):
|
||||
self._alpha = alpha
|
||||
self._beta = beta
|
||||
self._weight_sum = 0.0
|
||||
self.weight = np.zeros(size, dtype=np.float64)
|
||||
self._amortization_freq = 50
|
||||
self._amortization_counter = 0
|
||||
self._replace = replace
|
||||
self._meta.__dict__['weight'] = np.zeros(size, dtype=np.float64)
|
||||
|
||||
def add(self,
|
||||
obs: Union[dict, np.ndarray],
|
||||
@ -338,9 +341,9 @@ class PrioritizedReplayBuffer(ReplayBuffer):
|
||||
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.weight[self._index]
|
||||
# we have to sacrifice some convenience for speed :(
|
||||
self._meta.__dict__['weight'][self._index]
|
||||
self._add_to_buffer('weight', np.abs(weight) ** self._alpha)
|
||||
super().add(obs, act, rew, done, obs_next, info, policy)
|
||||
self._check_weight_sum()
|
||||
@ -414,18 +417,16 @@ class PrioritizedReplayBuffer(ReplayBuffer):
|
||||
- self.weight[indice].sum()
|
||||
self.weight[indice] = np.power(np.abs(new_weight), self._alpha)
|
||||
|
||||
def __getitem__(self, index: Union[str, slice, np.ndarray]) -> Batch:
|
||||
if isinstance(index, str):
|
||||
return getattr(self, index)
|
||||
def __getitem__(self, index: Union[slice, np.ndarray]) -> Batch:
|
||||
return Batch(
|
||||
obs=self.get(index, 'obs'),
|
||||
act=self.get(index, 'act', stack_num=0),
|
||||
act=self.act[index],
|
||||
# act_=self.get(index, 'act'), # stacked action, for RNN
|
||||
rew=self.get(index, 'rew', stack_num=0),
|
||||
done=self.get(index, 'done', stack_num=0),
|
||||
rew=self.rew[index],
|
||||
done=self.done[index],
|
||||
obs_next=self.get(index, 'obs_next'),
|
||||
info=self.get(index, 'info'),
|
||||
weight=self.get(index, 'weight', stack_num=0),
|
||||
weight=self.weight[index],
|
||||
policy=self.get(index, 'policy'),
|
||||
)
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user