Abstract
In large and continuous state-action spaces reinforcement learning heavily relies on function approximation techniques. Tile coding is a well-known function approximator that has been successfully applied to many reinforcement learning tasks. In this paper we introduce the hyperplane tile coding, in which the usual tiles are replaced by parameterized hyperplanes that approximate the action-value function. We compared the performance of hyperplane tile coding with the usual tile coding on three well-known benchmark problems. Our results suggest that the hyperplane tiles improve the generalization capabilities of the tile coding approximator: in the hyperplane tile coding broad generalizations over the problem space result only in a soft degradation of the performance, whereas in the usual tile coding they might dramatically affect the performance.
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References
Boyan, J.A., Moore, A.W.: Generalization in reinforcement learning: Safely approximating the value function. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems, vol. 7, pp. 369–376. The MIT Press, Cambridge (1995)
Haykin, S.: Adaptive Filter Theory. Prentice-Hall information and system sciences series (2002)
Kretchmar, R., Anderson, C.: Comparison of CMACs and radial basis functions for local function approximators in reinforcement learning. In: Proceedings of the IEEE International Conference on Neural Networks, Houston, TX, pp. 834–837 (1997)
Reynolds, S.I.: Reinforcement Learning with Exploration. Ph.D thesis, School of Computer Science. The University of Birmingham, Birmingham, B15 2TT (December 2002)
Sherstov, A.A., Stone, P.: Function approximation via tile coding: Automating parameter choice. In: Zucker, J.-D., Saitta, L. (eds.) SARA 2005. LNCS, vol. 3607, pp. 194–205. Springer, Heidelberg (2005)
Sutton, R.S.: Gain adaptation beats least squares? In: Proceedings of the Seventh Yale Workshop on Adaptive and Learning Systems, pp. 161–166. Yale University, New Haven (1992)
Sutton, R.S.: Generalization in reinforcement learning: Successful examples using sparse coarse coding. In: Touretzky, D.S., Mozer, M.C., Hasselmo, M.E. (eds.) Advances in Neural Information Processing Systems, vol. 8, pp. 1038–1044. The MIT Press, Cambridge (1996)
Sutton, R.S., Barto, A.G.: Reinforcement Learning – An Introduction. MIT Press, Cambridge (1998)
Tesauro, G.: TD-gammon, a self-teaching backgammon program, achieves master-level play. Neural Computation 6(2), 215–219 (1994)
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Loiacono, D., Lanzi, P.L. (2008). Tile Coding Based on Hyperplane Tiles. In: Girgin, S., Loth, M., Munos, R., Preux, P., Ryabko, D. (eds) Recent Advances in Reinforcement Learning. EWRL 2008. Lecture Notes in Computer Science(), vol 5323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89722-4_14
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DOI: https://doi.org/10.1007/978-3-540-89722-4_14
Publisher Name: Springer, Berlin, Heidelberg
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