Inverse Reinforcement Learning from a Learning Agent

We consider the problem of inferring the reward function and predicting the future behavior of an agent that is learning. To do this, we generalize an existing Bayesian inverse reinforcement learning algorithm to allow the demonstrator’s policy to change over time, as a function of their experiences and to simultaneously infer the actor’s reward function and methods of learning and making decisions. We show experimentally that our algorithm outperforms its inverse reinforcement learning counterpart.

Authors

• Vincent T Kubala
• George Konidaris Brown University
• Amy Greenwald Brown University

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