Distributed No-regret Learning In Multi-agent Systems

Abstract

In this tutorial article, we give an overview of new challenges and representative results on distributed no-regret learning in multi-agent systems modeled as repeated unknown games. Four emerging game characteristics---dynamicity, incomplete and imperfect feedback, bounded rationality, and heterogeneity---that challenge canonical game models are explored. For each of the four characteristics, we illuminate its implications and ramifications in game modeling, notions of regret, feasible game outcomes, and the design and analysis of distributed learning algorithms.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyxu2020distributed

Related papers

• Sample-efficient Distributionally Robust Multi-agent Reinforcement Learning Via Online Interaction (2025)0.00
• Regret Bounds For Decentralized Learning In Cooperative Multi-agent Dynamical Systems (2020)0.00
• Breaking The Curse Of Multiagency In Robust Multi-agent Reinforcement Learning (2024)0.00
• Independent Learning In Stochastic Games (2021)6.77
• No-regret Learning In Unknown Games With Correlated Payoffs (2019)0.00
• Robust Cooperative Multi-agent Reinforcement Learning:a Mean-field Type Game Perspective (2024)0.00
• A Black-box Approach For Non-stationary Multi-agent Reinforcement Learning (2023)0.00
• Learning In Multi-memory Games Triggers Complex Dynamics Diverging From Nash Equilibrium (2023)0.00