Mei, Songzhu

RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization

2023 · arXiv (Cornell University)

Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks. In the context of Multi-Agent Reinforcement Learning (MARL), learning coordinated and decentralized policies that are sensitive …