Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2502.13430v1
• Date: Wed, 19 Feb 2025 05:04:10 GMT
• Title: Vision-Based Generic Potential Function for Policy Alignment in Multi-Agent Reinforcement Learning
• Authors: Hao Ma, Shijie Wang, Zhiqiang Pu, Siyao Zhao, Xiaolin Ai,
• Abstract summary: We propose a hierarchical vision-based reward shaping method to guide the policy of reinforcement learning to align with human common sense.<n>To help the policy adapt to uncertainty and changes in long-horizon tasks, the top layer features an adaptive skill selection module.<n>Our method achieves a higher win rate and effectively aligns the policy with human common sense.
• Score: 14.68673479535835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Guiding the policy of multi-agent reinforcement learning to align with human common sense is a difficult problem, largely due to the complexity of modeling common sense as a reward, especially in complex and long-horizon multi-agent tasks. Recent works have shown the effectiveness of reward shaping, such as potential-based rewards, to enhance policy alignment. The existing works, however, primarily rely on experts to design rule-based rewards, which are often labor-intensive and lack a high-level semantic understanding of common sense. To solve this problem, we propose a hierarchical vision-based reward shaping method. At the bottom layer, a visual-language model (VLM) serves as a generic potential function, guiding the policy to align with human common sense through its intrinsic semantic understanding. To help the policy adapts to uncertainty and changes in long-horizon tasks, the top layer features an adaptive skill selection module based on a visual large language model (vLLM). The module uses instructions, video replays, and training records to dynamically select suitable potential function from a pre-designed pool. Besides, our method is theoretically proven to preserve the optimal policy. Extensive experiments conducted in the Google Research Football environment demonstrate that our method not only achieves a higher win rate but also effectively aligns the policy with human common sense.

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