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

1 code implementation • NeurIPS 2023 • Siqi Shen, Chennan Ma, Chao Li, Weiquan Liu, Yongquan Fu, Songzhu Mei, Xinwang Liu, Cheng Wang

Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks.

Multi-agent Reinforcement Learning reinforcement-learning