Efficient model-based reinforcement learning for approximate online optimal control

• Autores: Rushikesh Kamalapurkar, Joel A. Rosenfeld, W.E. Dixon
• Localización: Automatica: A journal of IFAC the International Federation of Automatic Control, ISSN 0005-1098, Vol. 74, 2016, págs. 247-258
• Idioma: inglés
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• Resumen

  • Abstract An infinite horizon optimal regulation problem is solved online for a deterministic control-affine nonlinear dynamical system using a state following (StaF) kernel method to approximate the value function. Unlike traditional methods that aim to approximate a function over a large compact set, the StaF kernel method aims to approximate a function in a small neighborhood of a state that travels within a compact set. Simulation results demonstrate that stability and approximate optimality of the control system can be achieved with significantly fewer basis functions than may be required for global approximation methods.