Multi-Agent Inverse Reinforcement LearningSriraam Natarajan; Gautam Kunapuli; Kshitij Judah; Prasad Tadepalli; Kristian Kersting; Jude W. Shavlik
In: Sorin Draghici; Taghi M. Khoshgoftaar; Vasile Palade; Witold Pedrycz; M. Arif Wani; Xingquan Zhu (Hrsg.). The Ninth International Conference on Machine Learning and Applications. International Conference on Machine Learning (ICML-2010), December 12-14, USA, Pages 395-400, IEEE Computer Society, 2010.
Learning the reward function of an agent by observing its behavior is termed inverse reinforcement learning and has applications in learning from demonstration or apprenticeship learning. We introduce the problem of multi-agent inverse reinforcement learning, where reward functions of multiple agents are learned by observing their uncoordinated behavior. A centralized controller then learns to coordinate their behavior by optimizing a weighted sum of reward functions of all the agents. We evaluate our approach on a traffic-routing domain, in which a controller coordinates actions of multiple traffic signals to regulate traffic density. We show that the learner is not only able to match but even significantly outperform the expert.