Learning RoboCup-Keepaway with Kernels
author: Tobias Jung,
Department of Computer Science, University of Texas at Austin
published: Feb. 25, 2007, recorded: June 2006, views: 4695
published: Feb. 25, 2007, recorded: June 2006, views: 4695
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Description
We give another success story of using kernel-based methods to solve a dificult reinforcement learning problem, namely that of 3vs2 keepaway in RoboCup simulated soccer. Key challenges in keepaway are the high-dimensionality of the state space (rendering conventional grid-based function approximation like tilecoding infeasable) and the stochasticity due to noise and multiple learning agents needing to co- operate. We use approximate policy iteration with sparsified regular- ization networks to carry out policy evaluation. Preliminary results indicate that the behavior learned through our approach clearly out- performs the best results obtained with tilecoding by Stone et al.
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