Reinforcement learning with supervision by combining multiple learnings and expert advices

Proceedings of the American Control Conference

Volumn 2006, Issue , 2006, Pages 4159-4164

  Chang, Hyeong Soo ^a  

^a Sogang University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

BASE AGENTS; EXPERT ADVICES; REINFORCEMENT FUNCTION;

ADAPTIVE SYSTEMS; CONVERGENCE OF NUMERICAL METHODS; EXPERT SYSTEMS; FUNCTION EVALUATION; NUMERICAL METHODS;

REINFORCEMENT LEARNING;

EID: 34047195105     PISSN: 07431619     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. M. N. Ahmadabadi and M. Asadpour, "Expertness based cooperative Q-learning," IEEE Trans. an Systems, Man, and Cybernetics, part B. vol. 32, no. 1, pp. 66-76, 2002.
2. A. G. Barto, "Reinforcement Learning," in Handbook of Learning and Approximate Dynamic Programming, J. Si, A. G. Barto, W. B. Powell, and D. Wunsch (eds.), pp. 804-809, Wiley-IEEE Press, Piscataway, NJ, 2004.
3. A. G. Barto and M. T. Rosenstein, "Supervised Actor-Critic Reinforcement Learning," in Handbook of Learning and Approximate Dynamic Programming, J. Si, A. G. Barto, W. B. Powell, and D. Wunsch (eds.), pp. 359-380, Wiley-IEEE Press, Piscataway, NJ, 2004.
4. D. P. Bertsekas and J. N. Tsitsiklis, Neuro Dynamic Programming. Athena Scientific, 1996.
5. H. S. Chang, "An Ant System Based Exploration-Exploitation for Reinforcement Learning," in Proc. of the IEEE Conf. on Systems, Man, and Cybernetics, Vol. 4, 2004, pp. 3805-3810.
6. D. Corne, Fl Glover, and M. Dorigo (eds.), New Ideas in Optimization, McGraw-Hill, 1999.
7. C. Drummond, "Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks," J. of Artificial Intelligence Research, vol. 16, 2002, pp. 59-104.
8. M. Dorigo and G. Di Caro, "The ant colony optimization metaheuristic," New Ideas in Optimization, D. Corne, M. Dorigo (eds.), pp. 11-32, McGraw-Hill, NY, USA, 1999.
9. K. Driessens and S. Dzeroski, "Integrating experimentation and guidance in relational reinforcement learning," in Proc. of the 19th Int. Conf. on Machine Learning, 2002, pp. 115-122.
10. R. C. Eberhart and J. Kennedy, Swarm Intelligence, Morgan Kaufmann, 2001.
11. E. Even-Dar and Y. Mansour, "Learning rates for Q-learning," J. of Machine Learning Research, vol. 5, 2003, pp. 1-25.
12. V. Gullapalli and A. G. Barto, "Convergence of Indirect Adaptive Asynchronous Value Iteration Algorithms", Advances in Neural Information Processing Systems, J. D. Cowan, G. Tesauro, and J. Alspector (eds.), Morgan Kaufmann Publishers, Inc., vol. 6, 1994, pp. 695-702.
13. L. P. Kaelbling, M. L. Littman, and A. W. Moore, "Reinforcement Learning; A Survey," J. of Artificial Intelligence Research, vol. 4, 1996, pp. 237-285.
14. U. Kuter and D. Nau, "Using domain-configurable search control for probabilistic planning," in Proc of the National Conf. on Artificial Intelligence (AAAI), 2005.
15. L. J. Lin, "Self-improving reactive agents based on reinforcement learning, planning and teaching," Machine Learning, vol. 8, 1992, pp. 294-321.
16. R. Maclin and J.W. Shavlik, "Creating advice-taking reinforcement learners," Machine Learning, vol. 22, 1996, pp. 251-282.
17. T. Mitchell, Machine Learning, McGraw Hill, 1997.
18. K. Narendra and M. A. L. Thathachar, Learning Automata: An Introduction, Englewood Cliffs, NJ: Prentice Hall, 1989.
19. A. Y. Ng, D. Harada, and S. Russell, "Policy invariance under reward transformations: theory and application to reward shaping," in Proc. of the 16th Int. Conf. on Machine Learning, 1999, pp. 278-287.
20. M. L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
21. M. Rosenstein and A.G. Barto, "Reinforcement learning with super- vision by a stable controller," in Proc. of the American Control Conf., 2004, pp. 4517-4522.
22. S. Russell and A. L. Zimdars, "Q-decomposition for reinforcement learning agents," in Proc. of the 20th Int. Conf. on Machine Learning, 2003, pp. 278-287.
23. S. Singh, T. Jaakkola, M. Littman, and C. Szepesvari, "Convergence results for single-step on-policy reinforcement learning algorithms," Machine Learning, vol. 38, pp. 287-308, 2000.
24. R. Sutton and A. Barto, Reinforcement Learning. MIT Press, 2000.
25. R. Sutton, "Open theoretical questions in reinforcement learning," in Proc. of the 4th European Conference on Computational Learning Theory, EuroCOLT'99, Nordkirchen, Germany, 1999, pp. 11-17.
26. J. N. Tsitsiklis, "Asynchronous stochastic approximation and Q-learning," Machine Learning, vol. 16, pp. 185-202, 1994.
27. R. J. Williams and L. C. Baird, "Analysis of some incremental variants of policy iteration: first steps toward understanding actor-critic learning systems," Tech. Rep. NU-CCS-93-11. 1993.
28. D. Zeng and K. Sycara, "Using case-based reasoning as a reinforcement learning framework for optimization with changing criteria," in Proc. of the 7th Int. Conf. on Tools with Artificial Intelligence, (ICTAI'95), pp. 56-62, 1995.