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

Agent-based modeling (ABM) involves developing models in which agents make adaptive
decisions in a changing environment. Machine-learning (ML) based inference models can …

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

A major challenge of multiagent reinforcement learning (MARL) is the curse of multiagents,
where the size of the joint action space scales exponentially with the number of agents. This …

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 …

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 …

Value function approximation has demonstrated phenomenal empirical success in
reinforcement learning (RL). Nevertheless, despite a handful of recent progress on …

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 …

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 …

A unique challenge in Multi-Agent Reinforcement Learning (MARL) is the\emph {curse of
multiagency}, where the description length of the game as well as the complexity of many …