Complementary Roles of Policy Update Constraints
In reinforcement learning, a common technique to stabilize training is to 'clip' the probability ratio between a new policy and a reference policy, preventing any single update step from being excessively large. However, relying solely on this clipping mechanism can still lead to instability over many updates. Explain why clipping alone might be insufficient and how incorporating a 'policy divergence penalty' into the objective function addresses this remaining issue.
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Proximal Policy Optimization (PPO)
In a reinforcement learning context, a policy is updated by maximizing an objective function. Consider an objective function that incorporates two distinct mechanisms to control the size of policy updates relative to a reference policy:
- A 'clipping' mechanism that puts a hard limit on the probability ratio between the new and reference policies, effectively creating a boundary beyond which the objective does not increase for a given sample.
- A 'penalty' term that is subtracted from the objective, with its magnitude increasing as the new policy diverges from the reference policy across all samples.
What is the most accurate analytical reason for using both of these mechanisms together, rather than relying on just one?
Diagnosing Training Instability in Reinforcement Learning
Complementary Roles of Policy Update Constraints
Composite Objective for PPO-Clip