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Asymmetric Effect of Upper-Bound Clipping
In a policy gradient algorithm, a clipping function is defined as min(ratio, 1 + ε), where ratio is the probability ratio of the new policy to the old policy, and ε is a small positive hyperparameter. Consider two scenarios for a given state-action pair:
- The advantage is positive (+3.0) and the ratio is 1.8.
- The advantage is negative (-3.0) and the ratio is 1.8.
Assuming ε = 0.2, explain how the clipping function affects the magnitude of the policy update in each scenario and analyze the reasoning behind this differential treatment.
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Clipped Utility Function with Upper-Bound Clipping
Consider a reinforcement learning agent being trained with a policy gradient method. For a given state-action pair, the ratio of the new policy's probability to the old policy's probability is 3.0. The estimated advantage for this action is positive. The algorithm incorporates a clipping mechanism defined as
min(ratio, 1 + ε), whereεis set to 0.2. What is the primary effect of this mechanism on the policy update for this specific step?Asymmetric Effect of Upper-Bound Clipping
A policy update mechanism uses a function to adjust the policy probability ratio, defined as
min(ratio, 1 + ε). Givenε = 0.2, match each originalratiovalue on the left with its corresponding adjusted value on the right after the function is applied.