Temporal abstraction in temporal-difference networks

Advances in Neural Information Processing Systems

Volumn , Issue , 2005, Pages 1313-1320

  Sutton, Richard S ^a   Rafols, Eddie J ^a   Koop, Anna ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ELIGIBILITY TRACES; FUNCTION APPROXIMATION; PRIMARY CONTRIBUTION; SEQUENCE OF ACTIONS; TEMPORAL ABSTRACTION; TIME INTERVAL; TIME STEP;

ABSTRACTING; APPROXIMATION ALGORITHMS;

FORECASTING;

EID: 84864067372     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. Jaeger, H. (2000). Observable operator models for discrete stochastic time series. Neural Computation, 12(6):1371-1398. MIT Press.
2. Littman, M., Sutton, R. S., & Singh, S. (2002). Predictive representations of state. In T. G. Dietterich, S. Becker and Z. Ghahramani (eds.), Advances In Neural Information Processing Systems 14, pp. 1555-1561. MIT Press.
3. Precup, D., Sutton, R. S., & Dasgupta, S. (2001). Off-policy temporal-difference learning with function approximation. In C. E. Brodley, A. P. Danyluk (eds.), Proceedings of the Eighteenth International Conference on Machine Learning, pp. 417-424. San Francisco, CA: Morgan Kaufmann.
4. Rafols, E. J., Ring, M., Sutton, R.S., & Tanner, B. (2005). Using predictive representations to improve generalization in reinforcement learning. To appear in Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence.
5. Rivest, R. L., & Schapire, R. E. (1987). Diversity-based inference of finite automata. In Proceedings of the Twenty Eighth Annual Symposium on Foundations of Computer Science, (pp. 78-87). IEEE Computer Society.
6. Singh, S., James, M. R., & Rudary, M. R. (2004). Predictive state representations: A new theory for modeling dynamical systems. In Uncertainty in Artificial Intelligence: Proceedings of the Twentieth Conference in Uncertainty in Artificial Intelligence, (pp. 512-519). AUAI Press.
7. Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
8. Sutton, R. S., Precup, D., Singh, S. (1999). Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence, 112, pp. 181-211.
9. Sutton, R. S., & Tanner, B. (2005). Temporal-difference networks. To appear in Neural Information Processing Systems Conference 17.
10. Tanner, B., Sutton, R. S. (2005) Temporal-difference networks with history. To appear in Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence.