42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
import tensorflow as tf
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def ppo_clip(sampled_action, advantage, clip_param, pi, pi_old):
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"""
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the clip loss in ppo paper
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:param sampled_action: placeholder of sampled actions during interaction with the environment
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:param advantage: placeholder of estimated advantage values.
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:param clip param: float or Tensor of type float.
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:param pi: current `policy` to be optimized
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:param pi_old: old `policy` for computing the ppo loss as in Eqn. (7) in the paper
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"""
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log_pi_act = pi.log_prob(sampled_action)
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log_pi_old_act = pi_old.log_prob(sampled_action)
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ratio = tf.exp(log_pi_act - log_pi_old_act)
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clipped_ratio = tf.clip_by_value(ratio, 1. - clip_param, 1. + clip_param)
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ppo_clip_loss = -tf.reduce_mean(tf.minimum(ratio * advantage, clipped_ratio * advantage))
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return ppo_clip_loss
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def vanilla_policy_gradient(sampled_action, reward, pi, baseline="None"):
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"""
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vanilla policy gradient
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:param sampled_action: placeholder of sampled actions during interaction with the environment
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:param reward: placeholder of reward the 'sampled_action' get
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:param pi: current 'policy' to be optimized
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:param baseline: the baseline method used to reduce the variance, default is 'None'
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:return:
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"""
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log_pi_act = pi.log_prob(sampled_action)
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vanilla_policy_gradient_loss = tf.reduce_mean(reward * log_pi_act)
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# TODO： Different baseline methods like REINFORCE, etc.
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return vanilla_policy_gradient_loss
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def temporal_difference_loss():
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pass
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def deterministic_policy_gradient():
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pass |