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。
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
 
 
 
 
 

591 行
22 KiB

import logging
from enum import Enum
from typing import Callable, Dict, List, Tuple, NamedTuple
import numpy as np
from mlagents.tf_utils import tf
from mlagents.trainers.exception import UnityTrainerException
from mlagents.trainers.brain import CameraResolution
logger = logging.getLogger("mlagents.trainers")
ActivationFunction = Callable[[tf.Tensor], tf.Tensor]
EncoderFunction = Callable[
[tf.Tensor, int, ActivationFunction, int, str, bool], tf.Tensor
]
EPSILON = 1e-7
class EncoderType(Enum):
SIMPLE = "simple"
NATURE_CNN = "nature_cnn"
RESNET = "resnet"
class LearningRateSchedule(Enum):
CONSTANT = "constant"
LINEAR = "linear"
class NormalizerTensors(NamedTuple):
update_op: tf.Operation
steps: tf.Tensor
running_mean: tf.Tensor
running_variance: tf.Tensor
class LearningModel:
# Minimum supported side for each encoder type. If refactoring an encoder, please
# adjust these also.
MIN_RESOLUTION_FOR_ENCODER = {
EncoderType.SIMPLE: 20,
EncoderType.NATURE_CNN: 36,
EncoderType.RESNET: 15,
}
@staticmethod
def create_global_steps():
"""Creates TF ops to track and increment global training step."""
global_step = tf.Variable(
0, name="global_step", trainable=False, dtype=tf.int32
)
steps_to_increment = tf.placeholder(
shape=[], dtype=tf.int32, name="steps_to_increment"
)
increment_step = tf.assign(global_step, tf.add(global_step, steps_to_increment))
return global_step, increment_step, steps_to_increment
@staticmethod
def create_learning_rate(
lr_schedule: LearningRateSchedule,
lr: float,
global_step: tf.Tensor,
max_step: int,
) -> tf.Tensor:
if lr_schedule == LearningRateSchedule.CONSTANT:
learning_rate = tf.Variable(lr)
elif lr_schedule == LearningRateSchedule.LINEAR:
learning_rate = tf.train.polynomial_decay(
lr, global_step, max_step, 1e-10, power=1.0
)
else:
raise UnityTrainerException(
"The learning rate schedule {} is invalid.".format(lr_schedule)
)
return learning_rate
@staticmethod
def scaled_init(scale):
return tf.initializers.variance_scaling(scale)
@staticmethod
def swish(input_activation: tf.Tensor) -> tf.Tensor:
"""Swish activation function. For more info: https://arxiv.org/abs/1710.05941"""
return tf.multiply(input_activation, tf.nn.sigmoid(input_activation))
@staticmethod
def create_visual_input(
camera_parameters: CameraResolution, name: str
) -> tf.Tensor:
"""
Creates image input op.
:param camera_parameters: Parameters for visual observation.
:param name: Desired name of input op.
:return: input op.
"""
o_size_h = camera_parameters.height
o_size_w = camera_parameters.width
c_channels = camera_parameters.num_channels
visual_in = tf.placeholder(
shape=[None, o_size_h, o_size_w, c_channels], dtype=tf.float32, name=name
)
return visual_in
@staticmethod
def create_visual_input_placeholders(
camera_resolutions: List[CameraResolution]
) -> List[tf.Tensor]:
visual_in: List[tf.Tensor] = []
for i, camera_resolution in enumerate(camera_resolutions):
visual_input = LearningModel.create_visual_input(
camera_resolution, name="visual_observation_" + str(i)
)
visual_in.append(visual_input)
return visual_in
@staticmethod
def create_vector_input(
vec_obs_size: int, name: str = "vector_observation"
) -> tf.Tensor:
"""
Creates ops for vector observation input.
:param name: Name of the placeholder op.
:param vec_obs_size: Size of stacked vector observation.
:return:
"""
vector_in = tf.placeholder(
shape=[None, vec_obs_size], dtype=tf.float32, name=name
)
return vector_in
@staticmethod
def normalize_vector_obs(
vector_obs: tf.Tensor,
running_mean: tf.Tensor,
running_variance: tf.Tensor,
normalization_steps: tf.Tensor,
) -> tf.Tensor:
normalized_state = tf.clip_by_value(
(vector_obs - running_mean)
/ tf.sqrt(
running_variance / (tf.cast(normalization_steps, tf.float32) + 1)
),
-5,
5,
name="normalized_state",
)
return normalized_state
@staticmethod
def create_normalizer(vector_obs: tf.Tensor) -> NormalizerTensors:
vec_obs_size = vector_obs.shape[1]
steps = tf.get_variable(
"normalization_steps",
[],
trainable=False,
dtype=tf.int32,
initializer=tf.zeros_initializer(),
)
running_mean = tf.get_variable(
"running_mean",
[vec_obs_size],
trainable=False,
dtype=tf.float32,
initializer=tf.zeros_initializer(),
)
running_variance = tf.get_variable(
"running_variance",
[vec_obs_size],
trainable=False,
dtype=tf.float32,
initializer=tf.ones_initializer(),
)
update_normalization = LearningModel.create_normalizer_update(
vector_obs, steps, running_mean, running_variance
)
return NormalizerTensors(
update_normalization, steps, running_mean, running_variance
)
@staticmethod
def create_normalizer_update(
vector_input: tf.Tensor,
steps: tf.Tensor,
running_mean: tf.Tensor,
running_variance: tf.Tensor,
) -> tf.Operation:
# Based on Welford's algorithm for running mean and standard deviation, for batch updates. Discussion here:
# https://stackoverflow.com/questions/56402955/whats-the-formula-for-welfords-algorithm-for-variance-std-with-batch-updates
steps_increment = tf.shape(vector_input)[0]
total_new_steps = tf.add(steps, steps_increment)
# Compute the incremental update and divide by the number of new steps.
input_to_old_mean = tf.subtract(vector_input, running_mean)
new_mean = running_mean + tf.reduce_sum(
input_to_old_mean / tf.cast(total_new_steps, dtype=tf.float32), axis=0
)
# Compute difference of input to the new mean for Welford update
input_to_new_mean = tf.subtract(vector_input, new_mean)
new_variance = running_variance + tf.reduce_sum(
input_to_new_mean * input_to_old_mean, axis=0
)
update_mean = tf.assign(running_mean, new_mean)
update_variance = tf.assign(running_variance, new_variance)
update_norm_step = tf.assign(steps, total_new_steps)
return tf.group([update_mean, update_variance, update_norm_step])
@staticmethod
def create_vector_observation_encoder(
observation_input: tf.Tensor,
h_size: int,
activation: ActivationFunction,
num_layers: int,
scope: str,
reuse: bool,
) -> tf.Tensor:
"""
Builds a set of hidden state encoders.
:param reuse: Whether to re-use the weights within the same scope.
:param scope: Graph scope for the encoder ops.
:param observation_input: Input vector.
:param h_size: Hidden layer size.
:param activation: What type of activation function to use for layers.
:param num_layers: number of hidden layers to create.
:return: List of hidden layer tensors.
"""
with tf.variable_scope(scope):
hidden = observation_input
for i in range(num_layers):
hidden = tf.layers.dense(
hidden,
h_size,
activation=activation,
reuse=reuse,
name="hidden_{}".format(i),
kernel_initializer=tf.initializers.variance_scaling(1.0),
)
return hidden
@staticmethod
def create_visual_observation_encoder(
image_input: tf.Tensor,
h_size: int,
activation: ActivationFunction,
num_layers: int,
scope: str,
reuse: bool,
) -> tf.Tensor:
"""
Builds a set of resnet visual encoders.
:param image_input: The placeholder for the image input to use.
:param h_size: Hidden layer size.
:param activation: What type of activation function to use for layers.
:param num_layers: number of hidden layers to create.
:param scope: The scope of the graph within which to create the ops.
:param reuse: Whether to re-use the weights within the same scope.
:return: List of hidden layer tensors.
"""
with tf.variable_scope(scope):
conv1 = tf.layers.conv2d(
image_input,
16,
kernel_size=[8, 8],
strides=[4, 4],
activation=tf.nn.elu,
reuse=reuse,
name="conv_1",
)
conv2 = tf.layers.conv2d(
conv1,
32,
kernel_size=[4, 4],
strides=[2, 2],
activation=tf.nn.elu,
reuse=reuse,
name="conv_2",
)
hidden = tf.layers.flatten(conv2)
with tf.variable_scope(scope + "/" + "flat_encoding"):
hidden_flat = LearningModel.create_vector_observation_encoder(
hidden, h_size, activation, num_layers, scope, reuse
)
return hidden_flat
@staticmethod
def create_nature_cnn_visual_observation_encoder(
image_input: tf.Tensor,
h_size: int,
activation: ActivationFunction,
num_layers: int,
scope: str,
reuse: bool,
) -> tf.Tensor:
"""
Builds a set of resnet visual encoders.
:param image_input: The placeholder for the image input to use.
:param h_size: Hidden layer size.
:param activation: What type of activation function to use for layers.
:param num_layers: number of hidden layers to create.
:param scope: The scope of the graph within which to create the ops.
:param reuse: Whether to re-use the weights within the same scope.
:return: List of hidden layer tensors.
"""
with tf.variable_scope(scope):
conv1 = tf.layers.conv2d(
image_input,
32,
kernel_size=[8, 8],
strides=[4, 4],
activation=tf.nn.elu,
reuse=reuse,
name="conv_1",
)
conv2 = tf.layers.conv2d(
conv1,
64,
kernel_size=[4, 4],
strides=[2, 2],
activation=tf.nn.elu,
reuse=reuse,
name="conv_2",
)
conv3 = tf.layers.conv2d(
conv2,
64,
kernel_size=[3, 3],
strides=[1, 1],
activation=tf.nn.elu,
reuse=reuse,
name="conv_3",
)
hidden = tf.layers.flatten(conv3)
with tf.variable_scope(scope + "/" + "flat_encoding"):
hidden_flat = LearningModel.create_vector_observation_encoder(
hidden, h_size, activation, num_layers, scope, reuse
)
return hidden_flat
@staticmethod
def create_resnet_visual_observation_encoder(
image_input: tf.Tensor,
h_size: int,
activation: ActivationFunction,
num_layers: int,
scope: str,
reuse: bool,
) -> tf.Tensor:
"""
Builds a set of resnet visual encoders.
:param image_input: The placeholder for the image input to use.
:param h_size: Hidden layer size.
:param activation: What type of activation function to use for layers.
:param num_layers: number of hidden layers to create.
:param scope: The scope of the graph within which to create the ops.
:param reuse: Whether to re-use the weights within the same scope.
:return: List of hidden layer tensors.
"""
n_channels = [16, 32, 32] # channel for each stack
n_blocks = 2 # number of residual blocks
with tf.variable_scope(scope):
hidden = image_input
for i, ch in enumerate(n_channels):
hidden = tf.layers.conv2d(
hidden,
ch,
kernel_size=[3, 3],
strides=[1, 1],
reuse=reuse,
name="layer%dconv_1" % i,
)
hidden = tf.layers.max_pooling2d(
hidden, pool_size=[3, 3], strides=[2, 2], padding="same"
)
# create residual blocks
for j in range(n_blocks):
block_input = hidden
hidden = tf.nn.relu(hidden)
hidden = tf.layers.conv2d(
hidden,
ch,
kernel_size=[3, 3],
strides=[1, 1],
padding="same",
reuse=reuse,
name="layer%d_%d_conv1" % (i, j),
)
hidden = tf.nn.relu(hidden)
hidden = tf.layers.conv2d(
hidden,
ch,
kernel_size=[3, 3],
strides=[1, 1],
padding="same",
reuse=reuse,
name="layer%d_%d_conv2" % (i, j),
)
hidden = tf.add(block_input, hidden)
hidden = tf.nn.relu(hidden)
hidden = tf.layers.flatten(hidden)
with tf.variable_scope(scope + "/" + "flat_encoding"):
hidden_flat = LearningModel.create_vector_observation_encoder(
hidden, h_size, activation, num_layers, scope, reuse
)
return hidden_flat
@staticmethod
def get_encoder_for_type(encoder_type: EncoderType) -> EncoderFunction:
ENCODER_FUNCTION_BY_TYPE = {
EncoderType.SIMPLE: LearningModel.create_visual_observation_encoder,
EncoderType.NATURE_CNN: LearningModel.create_nature_cnn_visual_observation_encoder,
EncoderType.RESNET: LearningModel.create_resnet_visual_observation_encoder,
}
return ENCODER_FUNCTION_BY_TYPE.get(
encoder_type, LearningModel.create_visual_observation_encoder
)
@staticmethod
def create_discrete_action_masking_layer(all_logits, action_masks, action_size):
"""
Creates a masking layer for the discrete actions
:param all_logits: The concatenated unnormalized action probabilities for all branches
:param action_masks: The mask for the logits. Must be of dimension [None x total_number_of_action]
:param action_size: A list containing the number of possible actions for each branch
:return: The action output dimension [batch_size, num_branches], the concatenated
normalized probs (after softmax)
and the concatenated normalized log probs
"""
action_idx = [0] + list(np.cumsum(action_size))
branches_logits = [
all_logits[:, action_idx[i] : action_idx[i + 1]]
for i in range(len(action_size))
]
branch_masks = [
action_masks[:, action_idx[i] : action_idx[i + 1]]
for i in range(len(action_size))
]
raw_probs = [
tf.multiply(tf.nn.softmax(branches_logits[k]) + EPSILON, branch_masks[k])
for k in range(len(action_size))
]
normalized_probs = [
tf.divide(raw_probs[k], tf.reduce_sum(raw_probs[k], axis=1, keepdims=True))
for k in range(len(action_size))
]
output = tf.concat(
[
tf.multinomial(tf.log(normalized_probs[k] + EPSILON), 1)
for k in range(len(action_size))
],
axis=1,
)
return (
output,
tf.concat([normalized_probs[k] for k in range(len(action_size))], axis=1),
tf.concat(
[
tf.log(normalized_probs[k] + EPSILON)
for k in range(len(action_size))
],
axis=1,
),
)
@staticmethod
def _check_resolution_for_encoder(
vis_in: tf.Tensor, vis_encoder_type: EncoderType
) -> None:
min_res = LearningModel.MIN_RESOLUTION_FOR_ENCODER[vis_encoder_type]
height = vis_in.shape[1]
width = vis_in.shape[2]
if height < min_res or width < min_res:
raise UnityTrainerException(
f"Visual observation resolution ({width}x{height}) is too small for"
f"the provided EncoderType ({vis_encoder_type.value}). The min dimension is {min_res}"
)
@staticmethod
def create_observation_streams(
visual_in: List[tf.Tensor],
vector_in: tf.Tensor,
num_streams: int,
h_size: int,
num_layers: int,
vis_encode_type: EncoderType = EncoderType.SIMPLE,
stream_scopes: List[str] = None,
) -> List[tf.Tensor]:
"""
Creates encoding stream for observations.
:param num_streams: Number of streams to create.
:param h_size: Size of hidden linear layers in stream.
:param num_layers: Number of hidden linear layers in stream.
:param stream_scopes: List of strings (length == num_streams), which contains
the scopes for each of the streams. None if all under the same TF scope.
:return: List of encoded streams.
"""
activation_fn = LearningModel.swish
vector_observation_input = vector_in
final_hiddens = []
for i in range(num_streams):
# Pick the encoder function based on the EncoderType
create_encoder_func = LearningModel.get_encoder_for_type(vis_encode_type)
visual_encoders = []
hidden_state, hidden_visual = None, None
_scope_add = stream_scopes[i] if stream_scopes else ""
if len(visual_in) > 0:
for j, vis_in in enumerate(visual_in):
LearningModel._check_resolution_for_encoder(vis_in, vis_encode_type)
encoded_visual = create_encoder_func(
vis_in,
h_size,
activation_fn,
num_layers,
f"{_scope_add}main_graph_{i}_encoder{j}", # scope
False, # reuse
)
visual_encoders.append(encoded_visual)
hidden_visual = tf.concat(visual_encoders, axis=1)
if vector_in.get_shape()[-1] > 0: # Don't encode 0-shape inputs
hidden_state = LearningModel.create_vector_observation_encoder(
vector_observation_input,
h_size,
activation_fn,
num_layers,
scope=f"{_scope_add}main_graph_{i}",
reuse=False,
)
if hidden_state is not None and hidden_visual is not None:
final_hidden = tf.concat([hidden_visual, hidden_state], axis=1)
elif hidden_state is None and hidden_visual is not None:
final_hidden = hidden_visual
elif hidden_state is not None and hidden_visual is None:
final_hidden = hidden_state
else:
raise Exception(
"No valid network configuration possible. "
"There are no states or observations in this brain"
)
final_hiddens.append(final_hidden)
return final_hiddens
@staticmethod
def create_recurrent_encoder(input_state, memory_in, sequence_length, name="lstm"):
"""
Builds a recurrent encoder for either state or observations (LSTM).
:param sequence_length: Length of sequence to unroll.
:param input_state: The input tensor to the LSTM cell.
:param memory_in: The input memory to the LSTM cell.
:param name: The scope of the LSTM cell.
"""
s_size = input_state.get_shape().as_list()[1]
m_size = memory_in.get_shape().as_list()[1]
lstm_input_state = tf.reshape(input_state, shape=[-1, sequence_length, s_size])
memory_in = tf.reshape(memory_in[:, :], [-1, m_size])
half_point = int(m_size / 2)
with tf.variable_scope(name):
rnn_cell = tf.nn.rnn_cell.BasicLSTMCell(half_point)
lstm_vector_in = tf.nn.rnn_cell.LSTMStateTuple(
memory_in[:, :half_point], memory_in[:, half_point:]
)
recurrent_output, lstm_state_out = tf.nn.dynamic_rnn(
rnn_cell, lstm_input_state, initial_state=lstm_vector_in
)
recurrent_output = tf.reshape(recurrent_output, shape=[-1, half_point])
return recurrent_output, tf.concat([lstm_state_out.c, lstm_state_out.h], axis=1)
@staticmethod
def create_value_heads(
stream_names: List[str], hidden_input: tf.Tensor
) -> Tuple[Dict[str, tf.Tensor], tf.Tensor]:
"""
Creates one value estimator head for each reward signal in stream_names.
Also creates the node corresponding to the mean of all the value heads in self.value.
self.value_head is a dictionary of stream name to node containing the value estimator head for that signal.
:param stream_names: The list of reward signal names
:param hidden_input: The last layer of the Critic. The heads will consist of one dense hidden layer on top
of the hidden input.
"""
value_heads = {}
for name in stream_names:
value = tf.layers.dense(hidden_input, 1, name="{}_value".format(name))
value_heads[name] = value
value = tf.reduce_mean(list(value_heads.values()), 0)
return value_heads, value