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Coffee and Check-In
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Opening Remarks
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"Calibeating": Beating Forecasters at Their Own Game
Sergiu Hart (Hebrew University of Jerusalem)
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Break
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V-Learning: Simple, Efficient, Decentralized Algorithm for Multiagent RL
Chi Jin (Princeton University)
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What is the Statistical Complexity of Reinforcement Learning?
Sham Kakade (Harvard and MSR)
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Lunch
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No-Regret Learning in Time-Varying Zero-Sum Games
Haipeng Luo (University of Southern California)
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Policy Gradients in General-Sum Dynamic Games: When Do They Even Converge?
Eric Mazumdar (Caltech)
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Break
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The Complexity of Infinite-Horizon General-Sum Stochastic Games: Turn-Based and Simultaneous Play
Vidya Muthukumar (Georgia Institute of Technology)
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Reception
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Break
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Coffee and Check-In
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The Complexity of Markov Equilibrium in Stochastic Games
Noah Golowich (MIT)
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Break
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A Regret Minimization Approach to Mutli-Agent Control and RL
Elad Hazan (Princeton University and Google Research)
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Break
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Multi-Agent Reinforcement Learning in the High Population Regime
Tamer Başar (University of Illinois Urbana-Champaign)
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Lunch
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Learning Automata as Building Blocks for MARL
Ann Nowe (Vrije Universiteit Brussel)
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On Rewards in Multi-Agent Systems
Doina Precup (McGill University / DeepMind Montreal)
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Break
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Independent Learning in Stochastic Games
Kaiqing Zhang (MIT)
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Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning
Zhuoran Yang (UC Berkeley)
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Open Problem Session
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Coffee and Check-In
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Multi-Agent Reinforcement Learning Towards Zero-Shot Communication
Kalesha Bullard (DeepMind)
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Break
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Kernelized Multiplicative Weights for 0/1-Polyhedral Games: Bridging the Gap Between Learning in Extensive-Form and Normal-Form Games
Gabriele Farina (Carnegie Mellon University)
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Break
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General Game-Theoretic Multiagent Reinforcement Learning
Marc Lanctot (DeepMind)
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Lunch
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When Is Offline Two-Player Zero-Sum Markov Game Solvable?
Simon Du (University of Washington)
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Variants and Invariants in No-Regret Algorithms
Sylvain Sorin (Sorbonne Universite)
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Break
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What Does Machine Learning Offer Game Theory (And Vice Versa)?
Alex Peysakhovich (Meta AI)
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Multi-Player Bandits With No Collisions
Mark Sellke (Stanford)
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Coffee and Check-In
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Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games
Ioannis Panageas (UC Irvine)
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Break
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Learning and Equilibrium Refinements
Drew Fudenberg (MIT)
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Break
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No-Regret Learning in Extensive-Form Games
Amy Greenwald (Brown University)
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Lunch
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Learning Decentralized Policies in Multiagent Systems: How to Learn Efficiently and What Are the Learned Policies?
Na Li (Harvard University)
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Break
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The Role of Conventions in Adaptive Human-AI Interaction
Dorsa Sadigh (Stanford University)