title: Reinforcement learning via kernel temporal difference.
abstract: This paper introduces a kernel adaptive filter implemented with stochastic gradient on temporal differences, kernel Temporal Difference (TD)(λ), to estimate the state-action value function in reinforcement learning. The case λ=0 will be studied in this paper. Experimental results show the method's applicability for learning motor state decoding during a center-out reaching task performed by a monkey. The results are compared to the implementation of a time delay neural network (TDNN) trained with backpropagation of the temporal difference error. From the experiments, it is observed that kernel TD(0) allows faster convergence and a better solution than the neural network.
authors.affiliation: Department of Electrical and Computer Engineering, PO Box 116130 NEB 486, Bldg #33, University of Florida, Gainesville, FL 32611, USA. jihyebae@ufl.edu
authors.forename: Jihye
authors.initials: J
authors.lastname: Bae
authors.forename: Pratik
authors.initials: P
authors.lastname: Chhatbar
authors.forename: Joseph T
authors.initials: JT
authors.lastname: Francis
authors.forename: Justin C
authors.initials: JC
authors.lastname: Sanchez
authors.forename: Jose C
authors.initials: JC
authors.lastname: Principe
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D032701:Biomimetics / Q000379:methods; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D012054:Reinforcement, Psychology; D014584:User-Computer Interface