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