Reinforcement learning and its relationship to supervised learning

Handbook of Learning and Approximate Dynamic Programming

Volumn , Issue , 2004, Pages 47-63

  Barto, Andrew G ^a   Dietterich, Thomas G ^b  

^a University of Massachusetts Amherst   (United States)

^b Oregon State University   (United States)

Author keywords

Algorithm design and analysis; Learning; Loss measurement; Machine learning; Supervised learning; Training

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; PERSONNEL TRAINING; REINFORCEMENT LEARNING; SUPERVISED LEARNING;

ALGORITHM DESIGN AND ANALYSIS; APPROXIMATE DYNAMIC PROGRAMMING; LEARNING; LOSS MEASUREMENT; ON-MACHINES; REAL-WORLD;

DYNAMIC PROGRAMMING;

EID: 84986214645     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1109/9780470544785.ch2     Document Type: Chapter

References (37)

1. A. G. Barto, S. J Bradtke, and S. P. Singh, Learning to act using real-time dynamic programming, Artificial Intelligence, vol. 72, pp. 81-138, 1995.
2. J. Baxter and P. L. Bartlett, Infinite-horizon gradient-based policy search, Journal of Artificial Intelligence Research, vol. 15, pp. 319-350, 2001.
3. J. Baxter, P. L. Bartlett, and L. Weaver, Infinite-horizon gradient-based policy search: II. Gradient ascent algorithms and experiments, Journal of Artificial Intelligence Research, vol. 15, pp. 351-381, 2001.
4. D. P. Bertsekas, Dynamic Programming: Deterministic and Stochastic Models, Prentice-Hall, Englewood Cliffs, NJ, 1987.
5. D. P. Bertsekas and J. N. Tsitsiklis, Neuro-Dynamic Programming, Athena Scientific, Belmont, MA, 1996.
6. C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, Oxford, England, 1996.
7. J. A. Boyan, Least-squares temporal difference learning, in I. Bratko, and S. Dze-roski (eds.), Machine Learning: Proc. Of the 16th International Conference (ICML), 1999.
8. S. J. Bradtke and A. G. Barto, Linear least-squares algorithms for temporal difference learning, Machine Learning, vol. 22, pp. 33-57,1996.
9. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, Wadsworth and Brooks, Monterey, CA, 1984.
10. P. Dayan, Motivated reinforcement learning, in T. G. Dietterich, S. Becker, and Z. Ghahramani (eds.), Advances in Neural Information Processing Systems 14, Proc. Of the 2002 Conference, pp. 11-18, MIT Press, Cambridge, MA, 2003.
11. T. G. Dietterich and X. Wang, Batch value function approximation via support vectors, in T. G. Dietterich, S. Becker, and Z. Ghahramani (eds.), Advances in Neural Information Processing Systems 14, Proc. Of the 2002 Conference, pp. 1491-1498, MIT Press, Cambridge, MA, 2003.
12. R. Dawkins, The Blind Watchmaker, Norton, New York, 1986.
13. R. O. Duda, R E. Hart, and D. G. Stork, Pattern Classification, Second Edition, Wiley, New York, 2001.
14. A. A. Feldbaum, Optimal Control Systems, Academic Press, New York, 1965.
15. B. R. Hergenhahn and M. H. Olson, An Introduction to Theories of Learning (Sixth Edition), Prentice-Hall, Upper Saddle River, NJ, 2001.
16. M. J. Kearns and U. V. Vazirani, An Introduction to Computational Learning Theory, MIT Press, Cambridge, MA, 1994.
17. M. G. Lagoudakis and R. Parr, Reinforcement learning as classification: leveraging modem classifiers, in T. G. Fawcett, N. Mishra (eds.), Proc. 20th International Conference on Machine Learning, pp. 424-431, AAAI Press, Menlo Park, CA, 2003.
18. G. J. McLachlan and T. Krishnan, The EM Algorithms and Extensions, John Wiley & Sons, Inc., New York, 1997.
19. J. M. Mendel and R. W. McLaren, Reinforcement learning control and pattem recognition systems, in J. M. Mendel and K. S. Fu (eds.), Adaptive Learning and Pattern Recognition Systems: Theory and Applications, pp. 287-318, Academic Press, New York, 1970.
20. D. Michie and C. Sammut, Behavioural clones and cognitive skill models, in K. Furukawa, D. Michie, and S. Muggleton (eds.), Machine Intelligence 14: Applied Machine Intelligence, pp. 387-395, Oxford University Press, New York, 1996.
21. M. L. Minsky, Theory of Neural-Analog Reinforcement Systems and Its Application to the Brain-Model Problem, Ph.D. dissertation, Princeton University, 1954.
22. M. L. Minsky, Steps toward artificial intelligence, Proc. Of the Institute of Radio Engineers, vol. 49, pp. 8-30, 1961. E. A. Feigenbaum and J. Feldman (eds.), Computers and Thought, pp. 406-450, McGraw-Hill, New York, 1963.
23. T. Mitchell, Machine Learning, McGraw-Hill, New York, 1997.
24. A. W. Moore and C. G. Atkeson, Prioritized sweeping: reinforcement learning with less data and less real time, Machine Learning, vol. 13, pp. 103-130, 1993.
25. D. A. Pomerleau, Efficient training of artificial neural networks for autonomous navigation, Neural Computation, vol. 3, pp. 88-97, 1991.
26. J. R. Quinlan, C4.5: Programs for Empirical Learning, Morgan Kaufmann, San Francisco, 1993.
27. A. L. Samuel, Some studies in machine learning using the game of checkers, IBM Journal on Research and Development, vol. 3, pp. 211-229, 1959. Reprinted in E. A. Feigenbaum and J. Feldman (eds.), Computers and Thought, pp. 71-105, McGraw-Hill, New York, 1963.
28. H. A. Simon, Administrative Behavior, Macmillan, New York, 1947.
29. R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 1998.
30. R. S. Sutton, Learning to predict by the method of temporal differences, Machine Learning, vol. 3, pp. 9-44,1988.
31. R. S. Sutton, Generalization in reinforcement learning: successful examples using coarse coding, in D. S. Touretzky, M. C. Moser and M. E. Hesselmo (eds.), Advances in Neural Information Processing Systems, Proc. Of the 1995 Conference, pp. 1038-1044, MIT Press, Cambridge, MA, 1996.
32. G. J. Tesauro, Practical issues in temp oral difference learning, Machi ne Learning, vol. 8, pp. 217-257,1992.
33. G. J. Tesauro, TD-Gammon, A self-teaching backgammon program, achieves master-level play, Neural Computation, vol. 6, pp. 215-219, 1994.
34. G. Tesauro, Temporal Difference Learning and TD-Gammon, Communications of the ACM, vol. 28, pp. 58-68,1995.
35. E. L. Thorndike, Animal Intelligence, Hafher, Darien, CT, 1911.
36. A. M. Turing, Computing machinery and intelligence, Mind, vol. 59, pp. 433-460, 1950. Reprinted in E. A. Feigenbaum and J. Feldman (eds.), Computers and Thought, pp. 11-15, McGraw-Hill, New York, 1963.
37. X. Wang and T. G. Dietterich, Model-based policy gradient reinforcement learning, in T. G. Fawcett, N. Mishra (eds.), Proc. 20th International Conference on Machine Learning, pp. 776-783, AAAI Press, Menlo Park, CA, 2003.