Multiresolution state-space discretization method for Q-learning
ACC'09 Proceedings of the 2009 conference on American Control Conference
Impedance learning for robotic contact tasks using natural actor-critic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The Journal of Supercomputing
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This paper presents an improved adaptive-reinforcement learning control methodology for the problem of unmanned air vehicle morphing control. The reinforcement learning morphing control function that learns the optimal shape change policy is integrated with an adaptive dynamic inversion control trajectory tracking function. An episodic unsupervised learning simulation using the Q-learning method is developed to replace an earlier and less accurate actor-critic algorithm. Sequential function approximation, a Galerkin-based scattered data approximation scheme, replaces a K-nearest neighbors (KNN) method and is used to generalize the learning from previously experienced quantized states and actions to the continuous state-action space, all of which may not have been experienced before. The improved method showed smaller errors and improved learning of the optimal shape compared to the KNN.