Learn what the term “proximal” means in the context of Proximal Policy Optimization (PPO), a reinforcement learning algorithm that trains an agent’s policy with a novel objective function and a constraint.
Table of Contents
Question
What does the “Proximal” in Proximal Policy Optimization refer to?
A. The algorithm’s proximity to the optimal policy
B. The use of a proximal gradient descent algorithm
C. The constraint that limits the distance between the new and old policy
D. The algorithm’s ability to handle proximal policies.
Answer
C. The constraint that limits the distance between the new and old policy
Explanation
The correct answer is C. The constraint that limits the distance between the new and old policy. Proximal Policy Optimization (PPO) is a reinforcement learning algorithm that trains an agent’s policy to perform well in complex tasks. PPO uses a novel objective function that encourages the agent to improve its policy while staying close to its previous policy. This is achieved by applying a constraint that penalizes the agent if the ratio of the new and old policy probabilities exceeds a certain threshold. This constraint ensures that the policy update is not too large and does not harm the agent’s performance. The term “proximal” refers to this constraint, which keeps the new policy in the vicinity (or proximity) of the old policy.
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