4 papers
NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria
Eason Yu, Tzu Hao Liu, Clément L. Canonne +4
Finding Nash equilibria in two-player zero-sum imperfect-information games remains a central challenge in multi-agent reinforcement learning. Recent multi-round regularization meth…
Fairness over Equality: Correcting Social Incentives in Asymmetric Sequential Social Dilemmas
Alper Demir, Hüseyin Aydın, Kale-ab Abebe Tessera +2
Sequential Social Dilemmas (SSDs) provide a key framework for studying how cooperation emerges when individual incentives conflict with collective welfare. In Multi-Agent Reinforce…
Redistributing Rewards Across Time and Agents for Multi-Agent Reinforcement Learning
Aditya Kapoor, Kale-ab Tessera, Mayank Baranwal +4
Credit assignmen, disentangling each agent's contribution to a shared reward, is a critical challenge in cooperative multi-agent reinforcement learning (MARL). To be effective, cre…
Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning
Aditya Kapoor, Sushant Swamy, Kale-ab Tessera +4
In multi-agent environments, agents often struggle to learn optimal policies due to sparse or delayed global rewards, particularly in long-horizon tasks where it is challenging to…