Online inverse reinforcement learning for nonlinear systems

Ryan Self; Michael Harlan; Rushikesh Kamalapurkar

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arXiv · 1811.10471

Online inverse reinforcement learning for nonlinear systems

Ryan Self·Michael Harlan·Rushikesh Kamalapurkar

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Abstract

This paper focuses on the development of an online inverse reinforcement learning (IRL) technique for a class of nonlinear systems. The developed approach utilizes observed state and input trajectories, and determines the unknown cost function and the unknown value function online. A parameter estimation technique is utilized to allow the developed IRL technique to determine the cost function weights in the presence of unknown dynamics. Simulation results are presented for a nonlinear system showing convergence of both unknown reward function weights and unknown dynamics.

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Ryan Self, Michael Harlan, Rushikesh Kamalapurkar. 2018-11-21. Online inverse reinforcement learning for nonlinear systems. https://arxiv.org/abs/1811.10471

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