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299 行
12 KiB
299 行
12 KiB
import numpy as np
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import h5py
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from typing import List, BinaryIO
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from mlagents_envs.exception import UnityException
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class BufferException(UnityException):
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"""
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Related to errors with the Buffer.
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"""
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pass
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class AgentBuffer(dict):
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"""
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AgentBuffer contains a dictionary of AgentBufferFields. Each agent has his own AgentBuffer.
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The keys correspond to the name of the field. Example: state, action
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"""
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class AgentBufferField(list):
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"""
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AgentBufferField is a list of numpy arrays. When an agent collects a field, you can add it to his
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AgentBufferField with the append method.
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"""
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def __init__(self):
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self.padding_value = 0
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super().__init__()
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def __str__(self):
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return str(np.array(self).shape)
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def append(self, element: np.ndarray, padding_value: float = 0.0) -> None:
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"""
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Adds an element to this list. Also lets you change the padding
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type, so that it can be set on append (e.g. action_masks should
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be padded with 1.)
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:param element: The element to append to the list.
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:param padding_value: The value used to pad when get_batch is called.
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"""
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super().append(element)
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self.padding_value = padding_value
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def extend(self, data: np.ndarray) -> None:
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"""
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Adds a list of np.arrays to the end of the list of np.arrays.
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:param data: The np.array list to append.
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"""
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self += list(np.array(data))
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def set(self, data):
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"""
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Sets the list of np.array to the input data
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:param data: The np.array list to be set.
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"""
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# Make sure we convert incoming data to float32 if it's a float
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dtype = None
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if data is not None and len(data) and isinstance(data[0], float):
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dtype = np.float32
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self[:] = []
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self[:] = list(np.array(data, dtype=dtype))
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def get_batch(
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self,
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batch_size: int = None,
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training_length: int = 1,
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sequential: bool = True,
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) -> np.ndarray:
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"""
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Retrieve the last batch_size elements of length training_length
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from the list of np.array
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:param batch_size: The number of elements to retrieve. If None:
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All elements will be retrieved.
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:param training_length: The length of the sequence to be retrieved. If
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None: only takes one element.
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:param sequential: If true and training_length is not None: the elements
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will not repeat in the sequence. [a,b,c,d,e] with training_length = 2 and
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sequential=True gives [[0,a],[b,c],[d,e]]. If sequential=False gives
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[[a,b],[b,c],[c,d],[d,e]]
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"""
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if sequential:
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# The sequences will not have overlapping elements (this involves padding)
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leftover = len(self) % training_length
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# leftover is the number of elements in the first sequence (this sequence might need 0 padding)
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if batch_size is None:
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# retrieve the maximum number of elements
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batch_size = len(self) // training_length + 1 * (leftover != 0)
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# The maximum number of sequences taken from a list of length len(self) without overlapping
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# with padding is equal to batch_size
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if batch_size > (len(self) // training_length + 1 * (leftover != 0)):
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raise BufferException(
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"The batch size and training length requested for get_batch where"
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" too large given the current number of data points."
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)
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if batch_size * training_length > len(self):
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padding = np.array(self[-1], dtype=np.float32) * self.padding_value
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return np.array(
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[padding] * (training_length - leftover) + self[:],
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dtype=np.float32,
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)
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else:
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return np.array(
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self[len(self) - batch_size * training_length :],
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dtype=np.float32,
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)
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else:
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# The sequences will have overlapping elements
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if batch_size is None:
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# retrieve the maximum number of elements
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batch_size = len(self) - training_length + 1
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# The number of sequences of length training_length taken from a list of len(self) elements
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# with overlapping is equal to batch_size
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if (len(self) - training_length + 1) < batch_size:
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raise BufferException(
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"The batch size and training length requested for get_batch where"
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" too large given the current number of data points."
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)
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tmp_list: List[np.ndarray] = []
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for end in range(len(self) - batch_size + 1, len(self) + 1):
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tmp_list += self[end - training_length : end]
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return np.array(tmp_list, dtype=np.float32)
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def reset_field(self) -> None:
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"""
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Resets the AgentBufferField
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"""
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self[:] = []
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def __init__(self):
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self.last_brain_info = None
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self.last_take_action_outputs = None
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super().__init__()
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def __str__(self):
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return ", ".join(["'{0}' : {1}".format(k, str(self[k])) for k in self.keys()])
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def reset_agent(self) -> None:
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"""
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Resets the AgentBuffer
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"""
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for k in self.keys():
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self[k].reset_field()
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self.last_brain_info = None
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self.last_take_action_outputs = None
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def __getitem__(self, key):
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if key not in self.keys():
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self[key] = self.AgentBufferField()
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return super().__getitem__(key)
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def check_length(self, key_list: List[str]) -> bool:
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"""
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Some methods will require that some fields have the same length.
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check_length will return true if the fields in key_list
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have the same length.
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:param key_list: The fields which length will be compared
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"""
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if len(key_list) < 2:
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return True
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length = None
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for key in key_list:
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if key not in self.keys():
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return False
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if (length is not None) and (length != len(self[key])):
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return False
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length = len(self[key])
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return True
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def shuffle(self, sequence_length: int, key_list: List[str] = None) -> None:
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"""
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Shuffles the fields in key_list in a consistent way: The reordering will
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be the same across fields.
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:param key_list: The fields that must be shuffled.
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"""
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if key_list is None:
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key_list = list(self.keys())
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if not self.check_length(key_list):
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raise BufferException(
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"Unable to shuffle if the fields are not of same length"
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)
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s = np.arange(len(self[key_list[0]]) // sequence_length)
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np.random.shuffle(s)
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for key in key_list:
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tmp: List[np.ndarray] = []
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for i in s:
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tmp += self[key][i * sequence_length : (i + 1) * sequence_length]
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self[key][:] = tmp
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def make_mini_batch(self, start: int, end: int) -> "AgentBuffer":
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"""
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Creates a mini-batch from buffer.
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:param start: Starting index of buffer.
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:param end: Ending index of buffer.
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:return: Dict of mini batch.
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"""
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mini_batch = AgentBuffer()
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for key in self:
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mini_batch[key] = self[key][start:end]
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return mini_batch
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def sample_mini_batch(
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self, batch_size: int, sequence_length: int = 1
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) -> "AgentBuffer":
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"""
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Creates a mini-batch from a random start and end.
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:param batch_size: number of elements to withdraw.
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:param sequence_length: Length of sequences to sample.
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Number of sequences to sample will be batch_size/sequence_length.
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"""
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num_seq_to_sample = batch_size // sequence_length
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mini_batch = AgentBuffer()
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buff_len = self.num_experiences
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num_sequences_in_buffer = buff_len // sequence_length
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start_idxes = (
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np.random.randint(num_sequences_in_buffer, size=num_seq_to_sample)
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* sequence_length
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) # Sample random sequence starts
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for i in start_idxes:
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for key in self:
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mini_batch[key].extend(self[key][i : i + sequence_length])
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return mini_batch
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def save_to_file(self, file_object: BinaryIO) -> None:
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"""
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Saves the AgentBuffer to a file-like object.
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"""
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with h5py.File(file_object, "w") as write_file:
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for key, data in self.items():
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write_file.create_dataset(key, data=data, dtype="f", compression="gzip")
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def load_from_file(self, file_object: BinaryIO) -> None:
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"""
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Loads the AgentBuffer from a file-like object.
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"""
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with h5py.File(file_object, "r") as read_file:
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for key in list(read_file.keys()):
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self[key] = AgentBuffer.AgentBufferField()
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# extend() will convert the numpy array's first dimension into list
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self[key].extend(read_file[key][()])
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def truncate(self, max_length: int, sequence_length: int = 1) -> None:
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"""
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Truncates the buffer to a certain length.
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This can be slow for large buffers. We compensate by cutting further than we need to, so that
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we're not truncating at each update. Note that we must truncate an integer number of sequence_lengths
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param: max_length: The length at which to truncate the buffer.
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"""
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current_length = self.num_experiences
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# make max_length an integer number of sequence_lengths
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max_length -= max_length % sequence_length
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if current_length > max_length:
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for _key in self.keys():
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self[_key] = self[_key][current_length - max_length :]
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def resequence_and_append(
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self,
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target_buffer: "AgentBuffer",
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key_list: List[str] = None,
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batch_size: int = None,
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training_length: int = None,
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) -> None:
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"""
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Takes in a batch size and training length (sequence length), and appends this AgentBuffer to target_buffer
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properly padded for LSTM use. Optionally, use key_list to restrict which fields are inserted into the new
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buffer.
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:param target_buffer: The buffer which to append the samples to.
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:param key_list: The fields that must be added. If None: all fields will be appended.
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:param batch_size: The number of elements that must be appended. If None: All of them will be.
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:param training_length: The length of the samples that must be appended. If None: only takes one element.
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"""
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if key_list is None:
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key_list = list(self.keys())
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if not self.check_length(key_list):
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raise BufferException(
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"The length of the fields {0} were not of same length".format(key_list)
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)
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for field_key in key_list:
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target_buffer[field_key].extend(
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self[field_key].get_batch(
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batch_size=batch_size, training_length=training_length
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)
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)
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@property
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def num_experiences(self) -> int:
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"""
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The number of agent experiences in the AgentBuffer, i.e. the length of the buffer.
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An experience consists of one element across all of the fields of this AgentBuffer.
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Note that these all have to be the same length, otherwise shuffle and append_to_update_buffer
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will fail.
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"""
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if self.values():
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return len(next(iter(self.values())))
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else:
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return 0
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