Model-based function approximation in reinforcement learning

Proceedings of the International Conference on Autonomous Agents

Volumn , Issue , 2007, Pages 670-677

  Jong, Nicholas K ^a   Stone, Peter ^a  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

Function approximation; Models; Reinforcement learning

Indexed keywords

APPROXIMATE MODELS; APPROXIMATE VALUE FUNCTIONS; BENCH-MARK PROBLEMS; CONTEMPORARY ALGORITHMS; ENVIRONMENT SYSTEMS; FINITE MODELS; FUNCTION APPROXIMATION; GENERIC METHODS; MODEL-BASED; MODEL-FREE; NOVEL ALGORITHMS; REAL-WORLD PROBLEMS; STATE SPACES; STOCHASTIC ENVIRONMENTS; TERM VALUES; VALUE FUNCTIONS;

AGENTS; APPROXIMATION ALGORITHMS; AUTONOMOUS AGENTS; EDUCATION; FUZZY CLUSTERING; REINFORCEMENT; REINFORCEMENT LEARNING;

LEARNING ALGORITHMS;

EID: 60349084848     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1329125.1329242     Document Type: Conference Paper

References (18)

1. C. G. Atkeson, A. W. Moore, and S. Schaal. Locally weighted learning for control. Artificial Intelligence Review, 11:75-113, 1997.
2. J. A. Boyan and A. W. Moore. Generalization in reinforcement learning: Safely approximating the value function. In Advances in Neural Information Processing Systems 7, 1995.
3. R. I. Brafman and M. Tennenholtz. R-MAX - a general polynomial time algorithm for near-optimal reinforcement learning. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, pages 953-958, 2001.
4. A. Dutech, T. Edmunds, J. Kok, M. Lagoudakis, M. Littman, M. Riedmiller, B. Russell, B. Scherrer, R. Sutton, S. Timmer, N. Vlassis, A. White, and S. Whiteson. Reinforcement learning benchmarks and bake-offs II. http://www.cs.rutgers.edu/~mlittman/topics/nips05-mdp/bakeoffs05.pdf, 2005.
5. R. Glaubius and W. D. Smart. Manifold representations for value-function approximation. In Proceedings of the Nineteenth National Conference on Artificial Intelligence, 2004.
6. G. J. Gordon. Stable function approximation in dynamic programming. In Proceedings of the Twelfth International Conference on Machine Learning, 1995.
7. M. Kearns and S. Singh. Near-optimal reinforcement learning in polynomial time. In Proceedings of the Fifteenth International Conference on Machine Learning, pages 260-268, 1998.
8. M. G. Lagoudakis and R. Parr. Least-squares policy iteration. Journal of Machine Learning Research, 4:1107-1149, 2003.
9. M. L. Littman, T. L. Dean, and L. P. Kaelbling. On the complexity of solving Markov decision problems. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, 1995.
10. S. Mahadevan. Samuel meets Amarel: Automating value function approximation using global state space analysis. In Proceedings of the Twentieth National Conference on Artificial Intelligence, 2005.
11. A. W. Moore and C. G. Atkeson. Prioritized sweeping: Reinforcement learning with less data and less real time. Machine Learning, 13:103-130, 1993.
12. D. Ormnoneit and Ś. Sen. Kernel-based reinforcement learning. Machine Learning, 49(2):161-178, Nov. 2002.
13. M. L. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, Inc., 1994.
14. M. Riedmiller. Neural fitted Q iteration - first experiences with a data efficient neural reinforcement learning method. In Proceedings of the European Conference on Machine Learning, 2005.
15. R. S. Sutton. Generalization in reinforcement learning: Successful examples using sparse coarse coding. In Advances in Neural Information Processing Systems 8, 1996.
16. R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, 1998.
17. C. Szepesvári and W. D. Smart. Interpolation-based Q-learning. In Proceedings of the Twenty-First International Conference on Machine Learning, 2004.
18. G. Tesauro. Temporal difference learning and TD-Gammon. Communications of the ACM, 38(3), Mar. 1995.