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Jun, 2018
多智能体逆强化学习用于确定性一般和随机博弈
Multi-agent Inverse Reinforcement Learning for General-sum Stochastic Games
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Xiaomin Lin, Stephen C. Adams, Peter A. Beling
TL;DR
该论文提出了针对多智能体逆向强化学习(MIRL)问题的五种变体解决方案,包括合作博弈、相关均衡博弈、纳什均衡博弈、对抗性均衡博弈和协调均衡博弈,并提出了一些新的方法来解决这些问题。
Abstract
This paper addresses the problem of
multi-agent inverse reinforcement learning
(MIRL) in a two-player general-sum
stochastic game
framework. Five variants of MIRL are considered: uCS-MIRL, advE-MIRL, cooE-MIRL, u
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