7 papers
Decentralized Best-Response-Based Learning in Two-Player Zero-Sum Stochastic Games: A Finite-Sample Analysis
Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar +2
We present a finite-sample analysis of decentralized learning in two-player zero-sum matrix games and stochastic games, with a focus on best-response-based learning algorithms. In…
Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners
Tinashe Handina, Tongxin Li, Kishan Panaganti +2
We study how an agent in a two-player repeated game can effectively utilize potentially imperfect advice when interacting with a no-regret learner. We characterize the advice lands…
Structure from Strategic Interaction & Uncertainty: Risk Sensitive Games for Robust Preference Learning
Max Horwitz, Jake Gonzales, Eric Mazumdar +1
A growing line of work reframes preference-based fine-tuning of large language models game-theoretically: Nash Learning from Human Feedback (NLHF) recasts the problem as a zero-sum…
Distributionally Robust Cooperative Multi-Agent Reinforcement Learning via Robust Value Factorization
Chengrui Qu, Christopher Yeh, Kishan Panaganti +2
Cooperative multi-agent reinforcement learning (MARL) commonly adopts centralized training with decentralized execution, where value-factorization methods enforce the individual-gl…
Convergent Q-Learning for Infinite-Horizon General-Sum Markov Games through Behavioral Economics
Yizhou Zhang, Eric Mazumdar
Risk-aversion and bounded rationality are two key characteristics of human decision-making. Risk-averse quantal-response equilibrium (RQE) is a solution concept that incorporates t…
Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning
Shangding Gu, Laixi Shi, Muning Wen +5
Driven by inherent uncertainty and the sim-to-real gap, robust reinforcement learning (RL) seeks to improve resilience against the complexity and variability in agent-environment s…