Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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import numpy as np
import h5py
from typing import List, BinaryIO
from mlagents_envs.exception import UnityException
class BufferException(UnityException):
"""
Related to errors with the Buffer.
"""
pass
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 __init__(self):
self.padding_value = 0
super().__init__()
def __str__(self):
return str(np.array(self).shape)
def append(self, element: np.ndarray, padding_value: float = 0.0) -> None:
"""
Adds an element to this list. Also lets you change the padding
type, so that it can be set on append (e.g. action_masks should
be padded with 1.)
:param element: The element to append to the list.
:param padding_value: The value used to pad when get_batch is called.
"""
super().append(element)
self.padding_value = padding_value
def extend(self, data: np.ndarray) -> None:
"""
Adds 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.
"""
# Make sure we convert incoming data to float32 if it's a float
dtype = None
if data is not None and len(data) and isinstance(data[0], float):
dtype = np.float32
self[:] = []
self[:] = list(np.array(data, dtype=dtype))
def get_batch(
self,
batch_size: int = None,
training_length: int = 1,
sequential: bool = True,
) -> np.ndarray:
"""
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 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."
)
if batch_size * training_length > len(self):
padding = np.array(self[-1], dtype=np.float32) * self.padding_value
return np.array(
[padding] * (training_length - leftover) + self[:],
dtype=np.float32,
)
else:
return np.array(
self[len(self) - batch_size * training_length :],
dtype=np.float32,
)
else:
# 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: List[np.ndarray] = []
for end in range(len(self) - batch_size + 1, len(self) + 1):
tmp_list += self[end - training_length : end]
return np.array(tmp_list, dtype=np.float32)
def reset_field(self) -> None:
"""
Resets the AgentBufferField
"""
self[:] = []
def __init__(self):
self.last_brain_info = None
self.last_take_action_outputs = None
super().__init__()
def __str__(self):
return ", ".join(["'{}' : {}".format(k, str(self[k])) for k in self.keys()])
def reset_agent(self) -> None:
"""
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().__getitem__(key)
def check_length(self, key_list: List[str]) -> bool:
"""
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
length = None
for key in key_list:
if key not in self.keys():
return False
if (length is not None) and (length != len(self[key])):
return False
length = len(self[key])
return True
def shuffle(self, sequence_length: int, key_list: List[str] = None) -> 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]]) // sequence_length)
np.random.shuffle(s)
for key in key_list:
tmp: List[np.ndarray] = []
for i in s:
tmp += self[key][i * sequence_length : (i + 1) * sequence_length]
self[key][:] = tmp
def make_mini_batch(self, start: int, end: int) -> "AgentBuffer":
"""
Creates a mini-batch from buffer.
:param start: Starting index of buffer.
:param end: Ending index of buffer.
:return: Dict of mini batch.
"""
mini_batch = AgentBuffer()
for key in self:
mini_batch[key] = self[key][start:end]
return mini_batch
def sample_mini_batch(
self, batch_size: int, sequence_length: int = 1
) -> "AgentBuffer":
"""
Creates a mini-batch from a random start and end.
:param batch_size: number of elements to withdraw.
:param sequence_length: Length of sequences to sample.
Number of sequences to sample will be batch_size/sequence_length.
"""
num_seq_to_sample = batch_size // sequence_length
mini_batch = AgentBuffer()
buff_len = self.num_experiences
num_sequences_in_buffer = buff_len // sequence_length
start_idxes = (
np.random.randint(num_sequences_in_buffer, size=num_seq_to_sample)
* sequence_length
) # Sample random sequence starts
for i in start_idxes:
for key in self:
mini_batch[key].extend(self[key][i : i + sequence_length])
return mini_batch
def save_to_file(self, file_object: BinaryIO) -> None:
"""
Saves the AgentBuffer to a file-like object.
"""
with h5py.File(file_object, "w") as write_file:
for key, data in self.items():
write_file.create_dataset(key, data=data, dtype="f", compression="gzip")
def load_from_file(self, file_object: BinaryIO) -> None:
"""
Loads the AgentBuffer from a file-like object.
"""
with h5py.File(file_object, "r") as read_file:
for key in list(read_file.keys()):
self[key] = AgentBuffer.AgentBufferField()
# extend() will convert the numpy array's first dimension into list
self[key].extend(read_file[key][()])
def truncate(self, max_length: int, sequence_length: int = 1) -> None:
"""
Truncates the buffer to a certain length.
This can be slow for large buffers. We compensate by cutting further than we need to, so that
we're not truncating at each update. Note that we must truncate an integer number of sequence_lengths
param: max_length: The length at which to truncate the buffer.
"""
current_length = self.num_experiences
# make max_length an integer number of sequence_lengths
max_length -= max_length % sequence_length
if current_length > max_length:
for _key in self.keys():
self[_key][:] = self[_key][current_length - max_length :]
def resequence_and_append(
self,
target_buffer: "AgentBuffer",
key_list: List[str] = None,
batch_size: int = None,
training_length: int = None,
) -> None:
"""
Takes in a batch size and training length (sequence length), and appends this AgentBuffer to target_buffer
properly padded for LSTM use. Optionally, use key_list to restrict which fields are inserted into the new
buffer.
:param target_buffer: The buffer which to append the samples to.
: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 = list(self.keys())
if not self.check_length(key_list):
raise BufferException(
f"The length of the fields {key_list} were not of same length"
)
for field_key in key_list:
target_buffer[field_key].extend(
self[field_key].get_batch(
batch_size=batch_size, training_length=training_length
)
)
@property
def num_experiences(self) -> int:
"""
The number of agent experiences in the AgentBuffer, i.e. the length of the buffer.
An experience consists of one element across all of the fields of this AgentBuffer.
Note that these all have to be the same length, otherwise shuffle and append_to_update_buffer
will fail.
"""
if self.values():
return len(next(iter(self.values())))
else:
return 0