您最多选择25个主题
主题必须以中文或者字母或数字开头，可以包含连字符 (-)，并且长度不得超过35个字符
227 行
10 KiB
227 行
10 KiB
import numpy as np
|
|
|
|
from unityagents.exception import UnityException
|
|
|
|
|
|
class BufferException(UnityException):
|
|
"""
|
|
Related to errors with the Buffer.
|
|
"""
|
|
pass
|
|
|
|
|
|
class Buffer(dict):
|
|
"""
|
|
Buffer contains a dictionary of AgentBuffer. The AgentBuffers are indexed by agent_id.
|
|
Buffer also contains an update_buffer that corresponds to the buffer used when updating the model.
|
|
"""
|
|
|
|
class AgentBuffer(dict):
|
|
"""
|
|
AgentBuffer contains a dictionary of AgentBufferFields. Each agent has his own AgentBuffer.
|
|
The keys correspond to the name of the field. Example: state, action
|
|
"""
|
|
|
|
class AgentBufferField(list):
|
|
"""
|
|
AgentBufferField is a list of numpy arrays. When an agent collects a field, you can add it to his
|
|
AgentBufferField with the append method.
|
|
"""
|
|
|
|
def __str__(self):
|
|
return str(np.array(self).shape)
|
|
|
|
def extend(self, data):
|
|
"""
|
|
Ads a list of np.arrays to the end of the list of np.arrays.
|
|
:param data: The np.array list to append.
|
|
"""
|
|
self += list(np.array(data))
|
|
|
|
def set(self, data):
|
|
"""
|
|
Sets the list of np.array to the input data
|
|
:param data: The np.array list to be set.
|
|
"""
|
|
self[:] = []
|
|
self[:] = list(np.array(data))
|
|
|
|
def get_batch(self, batch_size=None, training_length=1, sequential=True):
|
|
"""
|
|
Retrieve the last batch_size elements of length training_length
|
|
from the list of np.array
|
|
:param batch_size: The number of elements to retrieve. If None:
|
|
All elements will be retrieved.
|
|
:param training_length: The length of the sequence to be retrieved. If
|
|
None: only takes one element.
|
|
:param sequential: If true and training_length is not None: the elements
|
|
will not repeat in the sequence. [a,b,c,d,e] with training_length = 2 and
|
|
sequential=True gives [[0,a],[b,c],[d,e]]. If sequential=False gives
|
|
[[a,b],[b,c],[c,d],[d,e]]
|
|
"""
|
|
if training_length == 1:
|
|
# When the training length is 1, the method returns a list of elements,
|
|
# not a list of sequences of elements.
|
|
if batch_size is None:
|
|
# If batch_size is None : All the elements of the AgentBufferField are returned.
|
|
return np.array(self)
|
|
else:
|
|
# return the batch_size last elements
|
|
if batch_size > len(self):
|
|
raise BufferException("Batch size requested is too large")
|
|
return np.array(self[-batch_size:])
|
|
else:
|
|
# The training_length is not None, the method returns a list of SEQUENCES of elements
|
|
if not sequential:
|
|
# The sequences will have overlapping elements
|
|
if batch_size is None:
|
|
# retrieve the maximum number of elements
|
|
batch_size = len(self) - training_length + 1
|
|
# The number of sequences of length training_length taken from a list of len(self) elements
|
|
# with overlapping is equal to batch_size
|
|
if (len(self) - training_length + 1) < batch_size:
|
|
raise BufferException("The batch size and training length requested for get_batch where"
|
|
" too large given the current number of data points.")
|
|
tmp_list = []
|
|
for end in range(len(self) - batch_size + 1, len(self) + 1):
|
|
tmp_list += [np.array(self[end - training_length:end])]
|
|
return np.array(tmp_list)
|
|
if sequential:
|
|
# The sequences will not have overlapping elements (this involves padding)
|
|
leftover = len(self) % training_length
|
|
# leftover is the number of elements in the first sequence (this sequence might need 0 padding)
|
|
if batch_size is None:
|
|
# retrieve the maximum number of elements
|
|
batch_size = len(self) // training_length + 1 * (leftover != 0)
|
|
# The maximum number of sequences taken from a list of length len(self) without overlapping
|
|
# with padding is equal to batch_size
|
|
if batch_size > (len(self) // training_length + 1 * (leftover != 0)):
|
|
raise BufferException("The batch size and training length requested for get_batch where"
|
|
" too large given the current number of data points.")
|
|
tmp_list = []
|
|
padding = np.array(self[-1]) * 0
|
|
# The padding is made with zeros and its shape is given by the shape of the last element
|
|
for end in range(len(self), len(self) % training_length, -training_length)[:batch_size]:
|
|
tmp_list += [np.array(self[end - training_length:end])]
|
|
if (leftover != 0) and (len(tmp_list) < batch_size):
|
|
tmp_list += [np.array([padding] * (training_length - leftover) + self[:leftover])]
|
|
tmp_list.reverse()
|
|
return np.array(tmp_list)
|
|
|
|
def reset_field(self):
|
|
"""
|
|
Resets the AgentBufferField
|
|
"""
|
|
self[:] = []
|
|
|
|
def __init__(self):
|
|
self.last_brain_info = None
|
|
self.last_take_action_outputs = None
|
|
super(Buffer.AgentBuffer, self).__init__()
|
|
|
|
def __str__(self):
|
|
return ", ".join(["'{0}' : {1}".format(k, str(self[k])) for k in self.keys()])
|
|
|
|
def reset_agent(self):
|
|
"""
|
|
Resets the AgentBuffer
|
|
"""
|
|
for k in self.keys():
|
|
self[k].reset_field()
|
|
self.last_brain_info = None
|
|
self.last_take_action_outputs = None
|
|
|
|
def __getitem__(self, key):
|
|
if key not in self.keys():
|
|
self[key] = self.AgentBufferField()
|
|
return super(Buffer.AgentBuffer, self).__getitem__(key)
|
|
|
|
def check_length(self, key_list):
|
|
"""
|
|
Some methods will require that some fields have the same length.
|
|
check_length will return true if the fields in key_list
|
|
have the same length.
|
|
:param key_list: The fields which length will be compared
|
|
"""
|
|
if len(key_list) < 2:
|
|
return True
|
|
l = None
|
|
for key in key_list:
|
|
if key not in self.keys():
|
|
return False
|
|
if (l is not None) and (l != len(self[key])):
|
|
return False
|
|
l = len(self[key])
|
|
return True
|
|
|
|
def shuffle(self, key_list=None):
|
|
"""
|
|
Shuffles the fields in key_list in a consistent way: The reordering will
|
|
be the same across fields.
|
|
:param key_list: The fields that must be shuffled.
|
|
"""
|
|
if key_list is None:
|
|
key_list = list(self.keys())
|
|
if not self.check_length(key_list):
|
|
raise BufferException("Unable to shuffle if the fields are not of same length")
|
|
s = np.arange(len(self[key_list[0]]))
|
|
np.random.shuffle(s)
|
|
for key in key_list:
|
|
self[key][:] = [self[key][i] for i in s]
|
|
|
|
def __init__(self):
|
|
self.update_buffer = self.AgentBuffer()
|
|
super(Buffer, self).__init__()
|
|
|
|
def __str__(self):
|
|
return "update buffer :\n\t{0}\nlocal_buffers :\n{1}".format(str(self.update_buffer),
|
|
'\n'.join(
|
|
['\tagent {0} :{1}'.format(k, str(self[k])) for
|
|
k in self.keys()]))
|
|
|
|
def __getitem__(self, key):
|
|
if key not in self.keys():
|
|
self[key] = self.AgentBuffer()
|
|
return super(Buffer, self).__getitem__(key)
|
|
|
|
def reset_update_buffer(self):
|
|
"""
|
|
Resets the update buffer
|
|
"""
|
|
self.update_buffer.reset_agent()
|
|
|
|
def reset_all(self):
|
|
"""
|
|
Resets all the local local_buffers
|
|
"""
|
|
agent_ids = list(self.keys())
|
|
for k in agent_ids:
|
|
self[k].reset_agent()
|
|
|
|
def append_update_buffer(self, agent_id, key_list=None, batch_size=None, training_length=None):
|
|
"""
|
|
Appends the buffer of an agent to the update buffer.
|
|
:param agent_id: The id of the agent which data will be appended
|
|
:param key_list: The fields that must be added. If None: all fields will be appended.
|
|
:param batch_size: The number of elements that must be appended. If None: All of them will be.
|
|
:param training_length: The length of the samples that must be appended. If None: only takes one element.
|
|
"""
|
|
if key_list is None:
|
|
key_list = self[agent_id].keys()
|
|
if not self[agent_id].check_length(key_list):
|
|
raise BufferException("The length of the fields {0} for agent {1} where not of same length"
|
|
.format(key_list, agent_id))
|
|
for field_key in key_list:
|
|
self.update_buffer[field_key].extend(
|
|
self[agent_id][field_key].get_batch(batch_size=batch_size, training_length=training_length)
|
|
)
|
|
|
|
def append_all_agent_batch_to_update_buffer(self, key_list=None, batch_size=None, training_length=None):
|
|
"""
|
|
Appends the buffer of all agents to the update buffer.
|
|
:param key_list: The fields that must be added. If None: all fields will be appended.
|
|
:param batch_size: The number of elements that must be appended. If None: All of them will be.
|
|
:param training_length: The length of the samples that must be appended. If None: only takes one element.
|
|
"""
|
|
for agent_id in self.keys():
|
|
self.append_update_buffer(agent_id, key_list, batch_size, training_length)
|