229 citations · 664 across the 18 of their papers we have counts for
33 papers · 1 filter
Representation Learning for General-sum Low-rank Markov Games
Chengzhuo Ni, Yuda Song, Xuezhou Zhang +2
We study multi-agent general-sum Markov games with nonlinear function approximation. We focus on low-rank Markov games whose transition matrix admits a hidden low-rank structure on…
Learning Rationalizable Equilibria in Multiplayer Games
Yuanhao Wang, Dingwen Kong, Yu Bai +1
A natural goal in multiagent learning besides finding equilibria is to learn rationalizable behavior, where players learn to avoid iteratively dominated actions. However, even in t…
When Is Partially Observable Reinforcement Learning Not Scary?
Qinghua Liu, Alan Chung, Csaba Szepesvári +1
Applications of Reinforcement Learning (RL), in which agents learn to make a sequence of decisions despite lacking complete information about the latent states of the controlled sy…
Provable Reinforcement Learning with a Short-Term Memory
Yonathan Efroni, Chi Jin, Akshay Krishnamurthy +1
Real-world sequential decision making problems commonly involve partial observability, which requires the agent to maintain a memory of history in order to infer the latent states,…
V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL
Chi Jin, Qinghua Liu, Yuanhao Wang +1
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.…
A Simple Reward-free Approach to Constrained Reinforcement Learning
Sobhan Miryoosefi, Chi Jin
In constrained reinforcement learning (RL), a learning agent seeks to not only optimize the overall reward but also satisfy the additional safety, diversity, or budget constraints.…