Reinforcement learning: Past, present and future?

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1585, Issue , 1999, Pages 195-197

  Sutton, Richard S ^a  

^a AT AND T LABS RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 72949089205     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-48873-1_26     Document Type: Conference Paper

References (13)

1. Baxter, J., Tridgell, A., Weaver, L. (1998). KnightCap: A chess progream that learns by combining TD(λ) with game-tree search. Proceedings of the Fifteenth International Conference on Machine Learning, pp. 28-36.
2. Bertsekas, D. P., and Tsitsiklis, J. N. (1996). Neuro-Dynamic Programming. Athena Scientific, Belmont, MA.
3. Crites, R. H., and Barto, A. G. (1996). Improving elevator performance using re-inforcement learning. In Advances in Neural Information Processing Systems 9, pp. 1017-1023. MIT Press, Cambridge, MA.
4. McCallum, A. K. (1995) Reinforcement Learning with Selective Perception and Hidden State. University of Rochester PhD. thesis.
5. Nie, J., and Haykin, S. (1996). A dynamic channel assignment policy through Q-learning. CRL Report 334. Communications Research Laboratory, Mc-Master University, Hamilton, Ontario.
6. Precup, D., Sutton, R.S. (1998). Multi-time models for temporally abstract planning. Advances in Neural Information Processing Systems 11. MIT Press, Cambridge, MA.
7. Singh, S. P., and Bertsekas, D. (1997). Reinforcement learning for dynamic channel allocation in cellular telephone systems. In Advances in Neural Information Processing Systems 10, pp. 974-980. MIT Press, Cambridge, MA.
8. Sutton, R. S. (1988). Learning to predict by the methods of temporal differences. Machine Learning, 3:9-44.
9. Sutton, R. S., and Barto, A. G. (1998). Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA.
10. Sutton, R. S., Precup, D., Singh, S. (1998). Between MDPs and semi-MDPs: Learning, planning, and representing knowledge at multiple temporal scales. Technical Report 98-74, Department of Computer Science, University of Massachusetts.
11. Tesauro, G. J. (1995). Temporal difference learning and TD-Gammon. Communications of the ACM, 38:58-68.
12. Watkins, C. J. C. H. (1989). Learning from Delayed Rewards. Ph.D. thesis, Cambridge University.
13. Zhang, W., and Dietterich, T. G. (1996). High-performance job-shop scheduling with a time-delay TD(λ) network. In Advances in Neural Information Processing Systems 9, pp. 1024-1030. MIT Press, Cambridge, MA.