Inverse Reinforcement Learning in Large State Spaces via Function Approximation

28 Jul 2017Kun LiJoel W. Burdick

This paper introduces a new method for inverse reinforcement learning in large-scale and high-dimensional state spaces. To avoid solving the computationally expensive reinforcement learning problems in reward learning, we propose a function approximation method to ensure that the Bellman Optimality Equation always holds, and then estimate a function to maximize the likelihood of the observed motion... (read more)

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