The content introduces Pb-PPO, a new algorithm that dynamically adjusts the clipping bound in Proximal Policy Optimization (PPO) based on task preferences or human feedback. By utilizing a multi-armed bandit approach, Pb-PPO aims to enhance training outcomes and stability in various reinforcement learning tasks. The study compares Pb-PPO with traditional PPO variants and other online algorithms across different benchmarks, showcasing superior performance and stability. Additionally, the ethical implications of advancing PPO algorithms in various domains are discussed.
The study addresses the limitations of fixed clipping bounds in PPO by proposing Pb-PPO, which dynamically adjusts the clipping bound based on task preferences or human feedback. This innovative approach aims to improve training outcomes and stability in reinforcement learning tasks.
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by Ziqi Zhang,J... alle arxiv.org 03-11-2024
https://arxiv.org/pdf/2312.07624.pdfDomande più approfondite