Relational temporal difference learning

ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning

Volumn 2006, Issue , 2006, Pages 49-56

  Asgharbeygi, Nima ^a   Stracuzzi, David ^a   Langley, Pat ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION THEORY; FUNCTION EVALUATION; GAME THEORY; HIERARCHICAL SYSTEMS; MARKOV PROCESSES; MULTI AGENT SYSTEMS;

GENERAL GAME PLAYING REPOSITORY; MULTI AGENT MARKOV DECISION PROBLEMS; NONRELATIONAL METHODS; RELATIONAL TEMPORAL DIFFERENCE LEARNING;

LEARNING SYSTEMS;

EID: 33749265162     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Asadi, M., & Huber, M. (2004). State space reduction for hierarchical reinforcement learning. Proceedings of the Seventeenth International FLAIRS Conference (pp. 509-514). Miami Beach, FL.
2. Asgharbeygi, N., & Nejati, N. (2005). Guiding inference through relational reinforcement learning. Proceedings of the Fifteenth International Conference on Inductive Logic Programming (pp. 20-37). Bonn.
3. Baxter, J., Trigdell, A., & Weaver, L. (1998). Knightcap: A chess program that learns by combining TD(A) with game-tree search. Proceedings of the Fifteenth International Conference on Machine Learning (pp. 28-36). Madison, WI: Morgan Kaufmann.
4. Boutilier, C., Reiter, R., & Price, B. (2001). Symbolic dynamic programming for first order MDPs. Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (pp. 690-697). Seattle, Washington.
5. Dietterich, T. G. (2000). Hierarchical reinforcement learning with the MAXQ value function decomposition. Journal of Artificial Intelligence Research, 13, 227-303.
6. Dong, G., & Li, J. (1999). Efficient mining of emerging patterns: Discovering trends and differences. Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining (pp. 43-52). San Diego, CA.
7. Driessens, K., Ramon, J., & Blockeel, H. (2001). Speeding up relational reinforcement learning through the use of an incremental first order decision tree learning. Proceedings of the Twelfth European Conference on Machine Learning (pp. 97-108). Freiburg, Germany.
8. Dzeroski, S., Raedt, L. D., & Driessens, K. (2001). Relational reinforcement learning. Machine Learning, 43, 7-52.
9. Fawcett, T., & Utgoff, P. E. (1992). Automatic feature generation for problem solving systems. Proceedings of the Ninth International Workshop on Machine Learning (pp. 144-153). Aberdeen, Scotland.
10. Fern, A., Yoon, S. W., & Givan, R. (2004). Learning domain-specific control knowledge from random walks. Proceedings of the Fourteenth International Conference on Automated Planning and Scheduling (pp. 191-199). Whistler, British Columbia.
11. Guestrin, C., Koller, D., Gearhart, C., & Kanodia, N. (2003). Generalizing plans to new environments in relational mdps. Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (pp. 1003-1010). Acapulco, Mexico.
12. Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237-285.
13. Levinson, R., & Weber, R. (2000). Chess neighborhoods, function combination, and reinforcement learning. Proceedings of the Second International Conference on Computers and Games (pp. 133-150). Hamamatsu, Japan.
14. Littman, M. L. (1994). Markov games as a framework for multi-agent reinforcement learning. Proceedings of the Eleventh International Conference on Machine Learning (pp. 157-163). New Brunswick, NJ: Morgan Kaufmann.
15. Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
16. Szepesvari, C., & Littman, M. (1999). A unified analysis of value-function-based reinforcement learning algorithms. Neural Computation, 11, 2017-2060.
17. Tadepalli, P., Givan, R., & Driessens, K. (2004). Relational reinforcement learning: An overview. Proceedings of the ICML'04 Workshop on Relational Reinforcement Learning (pp. 1-9). Banff, Alberta.
18. Tesauro, G. (1994). TD-Gammon, a self-teaching backgammon program. Neural Computation, 6, 215-219.