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240 行
11 KiB
240 行
11 KiB
import numpy as np
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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 Buffer(dict):
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"""
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Buffer contains a dictionary of AgentBuffer. The AgentBuffers are indexed by agent_id.
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Buffer also contains an update_buffer that corresponds to the buffer used when updating the model.
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"""
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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 __str__(self):
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return str(np.array(self).shape)
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def extend(self, data):
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"""
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Ads 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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self[:] = []
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self[:] = list(np.array(data))
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def get_batch(self, batch_size=None, training_length=1, sequential=True):
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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 training_length == 1:
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# When the training length is 1, the method returns a list of elements,
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# not a list of sequences of elements.
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if batch_size is None:
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# If batch_size is None : All the elements of the AgentBufferField are returned.
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return np.array(self)
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else:
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# return the batch_size last elements
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if batch_size > len(self):
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raise BufferException("Batch size requested is too large")
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return np.array(self[-batch_size:])
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else:
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# The training_length is not None, the method returns a list of SEQUENCES of elements
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if not sequential:
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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("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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tmp_list = []
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for end in range(len(self) - batch_size + 1, len(self) + 1):
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tmp_list += [np.array(self[end - training_length:end])]
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return np.array(tmp_list)
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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("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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tmp_list = []
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padding = np.array(self[-1]) * 0
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# The padding is made with zeros and its shape is given by the shape of the last element
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for end in range(len(self), len(self) % training_length, -training_length)[:batch_size]:
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tmp_list += [np.array(self[end - training_length:end])]
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if (leftover != 0) and (len(tmp_list) < batch_size):
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tmp_list += [np.array([padding] * (training_length - leftover) + self[:leftover])]
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tmp_list.reverse()
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return np.array(tmp_list)
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def reset_field(self):
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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(Buffer.AgentBuffer, self).__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):
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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(Buffer.AgentBuffer, self).__getitem__(key)
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def check_length(self, key_list):
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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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l = 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 (l is not None) and (l != len(self[key])):
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return False
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l = len(self[key])
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return True
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def shuffle(self, key_list=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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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("Unable to shuffle if the fields are not of same length")
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s = np.arange(len(self[key_list[0]]))
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np.random.shuffle(s)
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for key in key_list:
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self[key][:] = [self[key][i] for i in s]
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def make_mini_batch(self, start, end):
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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 = {}
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for key in self:
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mini_batch[key] = np.array(self[key][start:end])
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return mini_batch
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def __init__(self):
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self.update_buffer = self.AgentBuffer()
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super(Buffer, self).__init__()
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def __str__(self):
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return "update buffer :\n\t{0}\nlocal_buffers :\n{1}".format(str(self.update_buffer),
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'\n'.join(
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['\tagent {0} :{1}'.format(k, str(self[k])) for
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k in self.keys()]))
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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.AgentBuffer()
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return super(Buffer, self).__getitem__(key)
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def reset_update_buffer(self):
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"""
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Resets the update buffer
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"""
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self.update_buffer.reset_agent()
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def reset_all(self):
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"""
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Resets all the local local_buffers
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"""
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agent_ids = list(self.keys())
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for k in agent_ids:
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self[k].reset_agent()
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def append_update_buffer(self, agent_id, key_list=None, batch_size=None, training_length=None):
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"""
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Appends the buffer of an agent to the update buffer.
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:param agent_id: The id of the agent which data will be appended
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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 = self[agent_id].keys()
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if not self[agent_id].check_length(key_list):
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raise BufferException("The length of the fields {0} for agent {1} where not of same length"
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.format(key_list, agent_id))
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for field_key in key_list:
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self.update_buffer[field_key].extend(
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self[agent_id][field_key].get_batch(batch_size=batch_size, training_length=training_length)
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)
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def append_all_agent_batch_to_update_buffer(self, key_list=None, batch_size=None, training_length=None):
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"""
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Appends the buffer of all agents to the update buffer.
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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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for agent_id in self.keys():
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self.append_update_buffer(agent_id, key_list, batch_size, training_length)
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