Sriraam Natarajan, Gautam Kunapuli amd Kshitij Judah, Prasad Tadepalli, Kristian Kersting & Jude Shavlik (2010).
Multi-Agent Inverse Reinforcement Learning. Proceedings of the International Conference on Machine Learning and Applications (ICMLA 2010), Washington DC, USA.
This publication is available in PDF.
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 multiagent 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.
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College of Letters and Science
University of Wisconsin - Madison
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