DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization

• URL: http://arxiv.org/abs/2207.05631v3
• Date: Fri, 5 Jan 2024 21:45:40 GMT
• Title: DGPO: Discovering Multiple Strategies with Diversity-Guided Policy Optimization
• Authors: Wentse Chen, Shiyu Huang, Yuan Chiang, Tim Pearce, Wei-Wei Tu, Ting Chen, Jun Zhu
• Abstract summary: We propose an on-policy algorithm that discovers multiple strategies for solving a given task. Unlike prior work, it achieves this with a shared policy network trained over a single run. Experimental results show that our method efficiently discovers diverse strategies in a wide variety of reinforcement learning tasks.
• Score: 34.40615558867965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most reinforcement learning algorithms seek a single optimal strategy that solves a given task. However, it can often be valuable to learn a diverse set of solutions, for instance, to make an agent's interaction with users more engaging, or improve the robustness of a policy to an unexpected perturbance. We propose Diversity-Guided Policy Optimization (DGPO), an on-policy algorithm that discovers multiple strategies for solving a given task. Unlike prior work, it achieves this with a shared policy network trained over a single run. Specifically, we design an intrinsic reward based on an information-theoretic diversity objective. Our final objective alternately constraints on the diversity of the strategies and on the extrinsic reward. We solve the constrained optimization problem by casting it as a probabilistic inference task and use policy iteration to maximize the derived lower bound. Experimental results show that our method efficiently discovers diverse strategies in a wide variety of reinforcement learning tasks. Compared to baseline methods, DGPO achieves comparable rewards, while discovering more diverse strategies, and often with better sample efficiency.

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