Recent years have witnessed significant advances in reinforcement learning (RL), which
has registered tremendous success in solving various sequential decision-making problems …

Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …

Despite the great empirical success of deep reinforcement learning, its theoretical
foundation is less well understood. In this work, we make the first attempt to theoretically …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

We obtain global, non-asymptotic convergence guarantees for independent learning
algorithms in competitive reinforcement learning settings with two agents (ie, zero-sum …

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 multi-agent reinforcement learning (MARL) in infinite-horizon discounted zero-sum
Markov games. We focus on the practical but challenging setting of decentralized MARL …

This paper presents a general mean-field game (GMFG) framework for simultaneous
learning and decision-making in stochastic games with a large population. It first establishes …

In this paper, we deepen the analysis of continuous time Fictitious Play learning algorithm to
the consideration of various finite state Mean Field Game settings (finite horizon, $\gamma …

We study infinite-horizon discounted two-player zero-sum Markov games, and develop a
decentralized algorithm that provably converges to the set of Nash equilibria under self-play …