Efficient model-based reinforcement learning for approximate online optimal

Abstract

In this paper the infinite horizon optimal regulation problem is solved online for a deterministic control-affine nonlinear dynamical system using the 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.

References in corpus (3)

• Concurrent learning-based approximate optimal regulation
• Model-based reinforcement learning for infinite-horizon approximate optimal tracking
• Concurrent learning-based online approximate feedback-Nash equilibrium solution of N-player nonzero-sum differential games

Cited by in corpus (11)

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• Verification for Machine Learning, Autonomy, and Neural Networks Survey
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• Lagrangian-based online safe reinforcement learning for state-constrained systems
• Online Output-Feedback Parameter and State Estimation for Second Order Linear Systems
• Output-feedback online optimal control for a class of nonlinear systems
• Structured Online Learning-based Control of Continuous-time Nonlinear Systems
• Reinforcement Learning-based Disturbance Rejection Control for Uncertain Nonlinear Systems
• Safety aware model-based reinforcement learning for optimal control of a class of output-feedback nonlinear systems
• Adaptive Observation-Based Efficient Reinforcement Learning for Uncertain Systems