Multi-Agent Inverse Reinforcement Learning

8 years 5 months ago
Multi-Agent Inverse Reinforcement Learning
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.
Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah,
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Authors Sriraam Natarajan, Gautam Kunapuli, Kshitij Judah, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik
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