Greedy feature replacement for online value function approximation
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Albus, J.S., 1971. A theory of cerebellar function. Math. Biosci., 10(1–2):25–61. [doi:10.1016/0025-5564(71)900 51-4]
Barto, A.G., Bradtke, S.J., Singh, S.P., 1995. Learning to act using real-time dynamic programming. Artif. Intell., 72(1–2):81–138. [doi:10.1016/0004-3702(94)00011-O]
Buro, M., 1999. From simple features to sophisticated evaluation functions. Proc. 1st Int. Conf. on Computers and Games, p.126–145. [doi:10.1007/3-540-48957-6_8]
de Hauwere, Y.M., Vrancx, P., Nowé, A., 2010. Generalized learning automata for multi-agent reinforcement learning. AI Commun., 23(4):311–324. [doi:10.3233/AIC-2010-0476]
Geramifard, A., Doshi, F., Redding, J., et al., 2011. Online discovery of feature dependencies. Proc. 28th Int. Conf. on Machine Learning, p.881–888.
Geramifard, A., Dann, C., How, J.P., 2013. Off-policy learning combined with automatic feature expansion for solving large MDPs. Proc. 1st Multidisciplinary Conf. on Reinforcement Learning and Decision Making, p.29–33.
Kaelbling, L.P., Littman, M.L., Moore, A.W., 1996. Reinforcement learning: a survey. J. Artif. Intell. Res., 4:237–285. [doi:10.1613/jair.301]
Kolter, J.Z., Ng, A.Y., 2009. Near-Bayesian exploration in polynomial time. Proc. 26th Annual Int. Conf. on Machine Learning, p. 513–520. [doi:10.1145/1553374. 1553441]
Lagoudakis, M.G., Parr, R., 2003. Least-squares policy iteration. J. Mach. Learn. Res., 4(6):1107–1149.
Pazis, J., Lagoudakis, M.G., 2009. Binary action search for learning continuous-action control policies. Proc. 26th Annual Int. Conf. on Machine Learning, p.793–800. [doi:10.1145/1553374.1553476]
Puterman, M.L., 1994. Markov Decision Processes-Discrete Stochastic Dynamic Programming. John Wiley & Sons, New York, NY, p.139–161.
Ratitch, B., Precup, D., 2004. Sparse distributed memories for on-line value-based reinforcement learning. Proc. 15th European Conf. on Machine Learning, p.347–358. [doi:10. 1007/978-3-540-30115-8_33]
Rummery, G.A., Niranjan, M., 1994. On-line Q-learning Using Connectionist Systems. Technical Report No. cued/f-infeng/tr166, Engineering Department, Cambridge University.
Singh, S., Jaakkola, T., Littman, M.L., et al., 2000. Convergence results for single-step on-policy reinforcement-learning algorithms. Mach. Learn., 38(3):287–308. [doi:10.1023/A: 1007678930559]
Singh, S.P., Sutton, R.S., 1996. Reinforcement learning with replacing eligibility traces. Mach. Learn., 22(1–3):123–158. [doi:10.1023/A:1018012322525]
Singh, S.P., Yee, R.C., 1994. An upper bound on the loss from approximate optimal-value functions. Mach. Learn., 16(3):227–233. [doi:10.1007/Bf00993308]
Sprague, N., Ballard, D., 2003. Multiple-goal reinforcement learning with modular sarsa(0). Proc. 18th Int. Joint Conf. on Artificial Intelligence, p.1445–1447.
Sturtevant, N.R., White, A.M., 2006. Feature construction for reinforcement learning in hearts. Proc. 5th Int. Conf. on Computers and Games, p.122–134. [doi:10.1007/978-3-540-75538-8_11]
Sutton, R.S., 1996. Generalization in reinforcement learning: successful examples using sparse coarse coding. Adv. Neur. Inform. Process. Syst., 8:1038–1044.
Sutton, R.S., Barto, A.G., 1998. Reinforcement Learning: an Introduction. MIT Press, Cambridge, MA, USA, p.3–25.
Tsitsiklis, J.N., 1994. Asynchronous stochastic approximation and Q-learning. Mach. Learn., 16(3):185–202. [doi:10. 1007/Bf00993306]
Tsitsiklis, J.N., van Roy, B., 1997. An analysis of temporal-difference learning with function approximation. IEEE Trans. Automat. Contr., 42(5):674–690. [doi:10.1109/9. 580874]
Watkins, C.J.C.H., Dayan, P., 1992. Q-learning. Mach. Learn., 8(3–4):279–292. [doi:10.1007/Bf00992698]
Whiteson, S., Taylor, M.E., Stone, P., 2007. Adaptive Tile Coding for Value Function Approximation. Technical Report No. AI-TR-07-339, University of Texas at Austin.