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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107 行
4.1 KiB

from mlagents.tf_utils import tf
from mlagents.trainers.models import LearningModel
class BehavioralCloningModel(LearningModel):
def __init__(
self,
brain,
h_size=128,
lr=1e-4,
n_layers=2,
m_size=128,
normalize=False,
use_recurrent=False,
seed=0,
):
LearningModel.__init__(self, m_size, normalize, use_recurrent, brain, seed)
num_streams = 1
hidden_streams = self.create_observation_streams(num_streams, h_size, n_layers)
hidden = hidden_streams[0]
self.dropout_rate = tf.placeholder(
dtype=tf.float32, shape=[], name="dropout_rate"
)
hidden_reg = tf.layers.dropout(hidden, self.dropout_rate)
if self.use_recurrent:
tf.Variable(
self.m_size, name="memory_size", trainable=False, dtype=tf.int32
)
self.memory_in = tf.placeholder(
shape=[None, self.m_size], dtype=tf.float32, name="recurrent_in"
)
hidden_reg, self.memory_out = self.create_recurrent_encoder(
hidden_reg, self.memory_in, self.sequence_length
)
self.memory_out = tf.identity(self.memory_out, name="recurrent_out")
if brain.vector_action_space_type == "discrete":
policy_branches = []
for size in self.act_size:
policy_branches.append(
tf.layers.dense(
hidden_reg,
size,
activation=None,
use_bias=False,
kernel_initializer=tf.initializers.variance_scaling(0.01),
)
)
self.action_probs = tf.concat(
[tf.nn.softmax(branch) for branch in policy_branches],
axis=1,
name="action_probs",
)
self.action_masks = tf.placeholder(
shape=[None, sum(self.act_size)], dtype=tf.float32, name="action_masks"
)
self.sample_action_float, _, normalized_logits = self.create_discrete_action_masking_layer(
tf.concat(policy_branches, axis=1), self.action_masks, self.act_size
)
tf.identity(normalized_logits, name="action")
self.sample_action = tf.cast(self.sample_action_float, tf.int32)
self.true_action = tf.placeholder(
shape=[None, len(policy_branches)],
dtype=tf.int32,
name="teacher_action",
)
self.action_oh = tf.concat(
[
tf.one_hot(self.true_action[:, i], self.act_size[i])
for i in range(len(self.act_size))
],
axis=1,
)
self.loss = tf.reduce_sum(
-tf.log(self.action_probs + 1e-10) * self.action_oh
)
self.action_percent = tf.reduce_mean(
tf.cast(
tf.equal(
tf.cast(tf.argmax(self.action_probs, axis=1), tf.int32),
self.sample_action,
),
tf.float32,
)
)
else:
self.policy = tf.layers.dense(
hidden_reg,
self.act_size[0],
activation=None,
use_bias=False,
name="pre_action",
kernel_initializer=tf.initializers.variance_scaling(0.01),
)
self.clipped_sample_action = tf.clip_by_value(self.policy, -1, 1)
self.sample_action = tf.identity(self.clipped_sample_action, name="action")
self.true_action = tf.placeholder(
shape=[None, self.act_size[0]], dtype=tf.float32, name="teacher_action"
)
self.clipped_true_action = tf.clip_by_value(self.true_action, -1, 1)
self.loss = tf.reduce_sum(
tf.squared_difference(self.clipped_true_action, self.sample_action)
)
optimizer = tf.train.AdamOptimizer(learning_rate=lr)
self.update = optimizer.minimize(self.loss)