Diverse Policies Converge in Reward-free Markov Decision Processe

Voices Powered byElevenlabs logo
Connected to paperThis paper is a preprint and has not been certified by peer review

Diverse Policies Converge in Reward-free Markov Decision Processe


Fanqi Lin, Shiyu Huang, Weiwei Tu


Reinforcement learning has achieved great success in many decision-making tasks, and traditional reinforcement learning algorithms are mainly designed for obtaining a single optimal solution. However, recent works show the importance of developing diverse policies, which makes it an emerging research topic. Despite the variety of diversity reinforcement learning algorithms that have emerged, none of them theoretically answer the question of how the algorithm converges and how efficient the algorithm is. In this paper, we provide a unified diversity reinforcement learning framework and investigate the convergence of training diverse policies. Under such a framework, we also propose a provably efficient diversity reinforcement learning algorithm. Finally, we verify the effectiveness of our method through numerical experiments.

Follow Us on


Add comment