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arXiv · 1506.00685

Model-based reinforcement learning for infinite-horizon approximate optimal tracking

Rushikesh Kamalapurkar·Lindsey Andrews·Patrick Walters·Warren E. Dixon
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Abstract

This paper provides an approximate online adaptive solution to the infinite-horizon optimal tracking problem for control-affine continuous-time nonlinear systems with unknown drift dynamics. Model-based reinforcement learning is used to relax the persistence of excitation condition. Model-based reinforcement learning is implemented using a concurrent learning-based system identifier to simulate experience by evaluating the Bellman error over unexplored areas of the state space. Tracking of the desired trajectory and convergence of the developed policy to a neighborhood of the optimal policy are established via Lyapunov-based stability analysis. Simulation results demonstrate the effectiveness of the developed technique.

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BibTeXRIS

Rushikesh Kamalapurkar, Lindsey Andrews, Patrick Walters, Warren E. Dixon. 2015-06-01. Model-based reinforcement learning for infinite-horizon approximate optimal tracking. https://doi.org/10.1109/tnnls.2015.2511658

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