BriefGPT.xyz
Oct, 2023
MIR2: 通过相互信息正则化迈向可证实鲁棒性的多智能体强化学习
MIR2: Towards Provably Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization
HTML
PDF
Simin Li, Ruixiao Xu, Jun Guo, Pu Feng, Jiakai Wang...
TL;DR
提出一种名为MIR2的方法,通过在常规情景训练策略并最小化互信息作为鲁棒正则化来提高多智能体强化学习的鲁棒性,实验证明MIR2在各种情况下都能比现有的max-min优化方法展现出更大的对抗性。
Abstract
Robust
multi-agent reinforcement learning
(MARL) necessitates
resilience
to uncertain or worst-case actions by unknown allies. Existing
max-min o
→