22 lines
888 B
Python
22 lines
888 B
Python
import tensorflow as tf
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def DPG(policy, action_value):
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"""
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construct the gradient tensor of deterministic policy gradient
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:param policy:
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:param action_value:
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:return: list of (gradient, variable) pairs
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"""
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trainable_variables = policy.trainable_variables
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critic_action_input = action_value._action_placeholder
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critic_value_loss = -tf.reduce_mean(action_value.value_tensor)
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policy_action_output = policy.action
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grad_ys = tf.gradients(critic_value_loss, critic_action_input)
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grad_policy_vars = tf.gradients(policy_action_output, trainable_variables, grad_ys=grad_ys)
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# TODO: this is slightly different from ddpg implementations in baselines, keras-rl and rllab. it uses sampled action (with noise) rather than directly connect the two networks
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grads_and_vars = zip(grad_policy_vars, trainable_variables)
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return grads_and_vars |