Asked about Policy gradients: from REINFORCE to PPO

Walk me through the PPO objective.

An answer that works

The ratio compares the new policy's probability of an action with the old policy's. Multiplied by the advantage, it says how much better the new policy is on that data. Clipping the ratio to a band around 1, and taking the minimum of the clipped and unclipped terms, removes the incentive to move the policy far in a single update.

Then they push further

Why is a large policy update so harmful in RL?

The policy decides what data is collected next. A bad update produces bad data, which produces worse updates, and training can collapse without recovering. In supervised learning the data does not depend on the model, so a bad step is corrected by the next batch.

What is the advantage and why use it instead of the return?

The return minus a baseline of expected return from that state. It keeps the gradient direction while greatly reducing its variance, since actions are judged relative to what was expected.

What gets marked up

Strong answers explain ratio, advantage, clip and min, and connect clipping to data depending on the policy.

Reading an answer is not being able to give one. The section that teaches this is Policy gradients: from REINFORCE to PPO, 6 hours of Reinforcement learning.

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