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Mar, 2014
二人零和博弈的多智能体逆强化学习
Multi-agent Inverse Reinforcement Learning for Zero-sum Games
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Xiaomin Lin, Peter A. Beling, Randy Cogill
TL;DR
本文提出了一种贝叶斯框架,用于解决多智能体逆强化学习问题,在多智能体对战场景下建立了一种理论基础,并针对双智能体零和MIRL问题提出了一种贝叶斯解决方法,结果表明,奖励先验中协方差结构比均值更重要。
Abstract
In this paper we introduce a
bayesian framework
for solving a class of problems termed
multi-agent inverse reinforcement learning
(MIRL). Compared to the well-known Inverse Reinforcement Learning (IRL) problem, M
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