Relativized Options: Choosing the Right Transformation

Proceedings, Twentieth International Conference on Machine Learning

Volumn 2, Issue , 2003, Pages 608-615

  Ravindran, Balaraman ^a   Barto, Andrew G ^a  

^a University of Massachusetts Amherst   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DECISION THEORY; MATHEMATICAL MODELS; MATHEMATICAL TRANSFORMATIONS; PUBLIC POLICY;

CANDIDATE TRANSFORMATIONS; HIERARCHICAL REINFORCEMENT LEARNING; MACRO OPERATORS; MINIMIZATION METHODS;

LEARNING SYSTEMS;

EID: 1942484796     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Agre, P. E. (1988). The dynamic structure of everyday life (Technical Report AITR-1085). Massachusetts Institute of Technology.
2. Dietterich, T. G. (2000). Hierarchical reinforcement learning with the MAXQ value function decomposition. Artificial Intelligence Research, 13, 227-303.
3. Doya, K., Samejima, K., Katagiri, K., & Kawato, M. (2002). Multiple model-based reinforcement learning. To appear in Neural Computation.
4. Givan, R., Dean, T., & Greig, M. (2003). Equivalence notions and model minimization in Markov decision processes. To appear in Artificial Intelligence.
5. Givan, R., Leach, S., & Dean, T. (2000). Bounded-parameter Markov decision processes. Artificial Intelligence, 122, 71-109.
6. Hartmanis, J., & Stearns, R. E. (1966). Algebraic structure theory of sequential machines. Englewood Cliffs, NJ: Prentice-Hall.
7. Haruno, M., Wolpert, D. M., & Kawato, M. (2001). MOSAIC model for sensorimotor learning and control. Neural Computation, 13, 2201-2220.
8. Hengst, B. (2002). Discovering hierarchy in reinforcement learning with HEXQ. Proceedings of the 19th International Conference on Machine Learning (pp. 243-250).
9. Iba, G. A. (1989). A heuristic approach to the discovery of macro-operators. Machine Learning, 3, 285-317.
10. Jonsson, A., & Barto, A. G. (2001). Automated state abstraction for options using the u-tree algorithm. Proceedings of Advances in Neural Information Processing Systems 13 (pp. 1054-1060). Cambridge, MA: MIT Press.
11. McGovern, A., & Barto, A. G. (2001). Automatic discovery of subgoals in reinforcement learning using diverse density. Proceedings of the 18th International Conference on Machine Learning ICML 2001 (pp. 361-368).
12. Parr, R., & Russell, S. (1997). Reinforcement learning with hierarchies of machines. Proceedings of Advances in Neural Information Processing Systems 10 (pp. 1043-1049). MIT Press.
13. Ravindran, B., & Barto, A. G. (2001). Symmetries and model minimization of Markov decision processes (Technical Report 01-43). University of Massachusetts, Amherst.
14. Ravindran, B., & Barto, A. G. (2002). Model minimization in hierarchical reinforcement learning. Proceedings of the Fifth Symposium on Abstraction, Reformulation and Approximation (SARA 2002) (pp. 196-211). New York, NY: Springer-Verlag.
15. Ravindran, B., & Barto, A. G. (2003). SMDP homomorphisms: An algebraic approach to abstraction in semi-Markov decision processes. To appear in the Proceedings of the Eighteenth Internatinal Joint Conference on Artificial Intelligence (IJCAI 2003).
16. Sutton, R. S., Precup, D., & Singh, S. (1999). Between MDPs and Semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence, 112, 181-211.
17. Wheeler, D. S., Fagg, A. H., & Grupen, R. A. (2002). Learning prospective pick and place behavior. Proceedings of the International Conference on Development and Learning (ICDL '02).