Abstract Model-based algorithms—algorithms that explore the environment through building
and utilizing an estimated model—are widely used in reinforcement learning practice and …

Self-play, where the algorithm learns by playing against itself without requiring any direct
supervision, has become the new weapon in modern Reinforcement Learning (RL) for …

We study model-free reinforcement learning (RL) algorithms for infinite-horizon average-
reward Markov decision process (MDP), which is more appropriate for applications that …

This paper studies policy optimization algorithms for multi-agent reinforcement learning. We
begin by proposing an algorithm framework for two-player zero-sum Markov Games in the …

Abstract Model-based reinforcement learning (RL), which finds an optimal policy using an
empirical model, has long been recognized as one of the cornerstones of RL. It is especially …

We study what dataset assumption permits solving offline two-player zero-sum Markov
games. In stark contrast to the offline single-agent Markov decision process, we show that …

Optimization of parameterized policies for reinforcement learning (RL) is an important and
challenging problem in artificial intelligence. Among the most common approaches are …

This paper considers the problem of designing optimal algorithms for reinforcement learning
in two-player zero-sum games. We focus on self-play algorithms which learn the optimal …

This paper considers offline multi-agent reinforcement learning. We propose the strategy-
wise concentration principle which directly builds a confidence interval for the joint strategy …

In this work, we develop provably efficient reinforcement learning algorithms for two-player
zero-sum Markov games with simultaneous moves. We consider a family of Markov games …