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75 行
2.9 KiB
75 行
2.9 KiB
from mlagents.tf_utils import tf
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from mlagents.trainers.policy.tf_policy import TFPolicy
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class BCModel:
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def __init__(
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self, policy: TFPolicy, learning_rate: float = 3e-4, anneal_steps: int = 0
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):
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"""
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Tensorflow operations to perform Behavioral Cloning on a Policy model
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:param policy: The policy of the learning algorithm
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:param lr: The initial learning Rate for behavioral cloning
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:param anneal_steps: Number of steps over which to anneal BC training
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"""
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self.policy = policy
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self.expert_visual_in = self.policy.visual_in
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self.obs_in_expert = self.policy.vector_in
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self.make_inputs()
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self.create_loss(learning_rate, anneal_steps)
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def make_inputs(self) -> None:
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"""
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Creates the input layers for the discriminator
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"""
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self.done_expert = tf.placeholder(shape=[None, 1], dtype=tf.float32)
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self.done_policy = tf.placeholder(shape=[None, 1], dtype=tf.float32)
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if self.policy.behavior_spec.is_action_continuous():
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action_length = self.policy.act_size[0]
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self.action_in_expert = tf.placeholder(
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shape=[None, action_length], dtype=tf.float32
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)
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self.expert_action = tf.identity(self.action_in_expert)
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else:
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action_length = len(self.policy.act_size)
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self.action_in_expert = tf.placeholder(
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shape=[None, action_length], dtype=tf.int32
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)
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self.expert_action = tf.concat(
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[
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tf.one_hot(self.action_in_expert[:, i], act_size)
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for i, act_size in enumerate(self.policy.act_size)
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],
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axis=1,
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)
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def create_loss(self, learning_rate: float, anneal_steps: int) -> None:
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"""
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Creates the loss and update nodes for the BC module
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:param learning_rate: The learning rate for the optimizer
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:param anneal_steps: Number of steps over which to anneal the learning_rate
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"""
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selected_action = self.policy.output
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if self.policy.use_continuous_act:
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self.loss = tf.reduce_mean(
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tf.squared_difference(selected_action, self.expert_action)
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)
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else:
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log_probs = self.policy.all_log_probs
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self.loss = tf.reduce_mean(
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-tf.log(tf.nn.softmax(log_probs) + 1e-7) * self.expert_action
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)
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if anneal_steps > 0:
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self.annealed_learning_rate = tf.train.polynomial_decay(
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learning_rate, self.policy.global_step, anneal_steps, 0.0, power=1.0
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)
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else:
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self.annealed_learning_rate = tf.Variable(learning_rate)
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optimizer = tf.train.AdamOptimizer(
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learning_rate=self.annealed_learning_rate, name="bc_adam"
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)
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self.update_batch = optimizer.minimize(self.loss)
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