Diverse Policies Converge in Reward-free Markov Decision Processe
This addresses the need for theoretical guarantees in diversity reinforcement learning, which is an emerging topic for decision-making tasks.
The paper tackles the problem of developing diverse policies in reinforcement learning by providing a unified framework and investigating their convergence, proposing a provably efficient algorithm that is verified through numerical experiments.
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.