A hierarchical approach to efficient reinforcement learning in deterministic domains

Proceedings of the International Conference on Autonomous Agents

Volumn 2006, Issue , 2006, Pages 313-319

  Diuk, Carlos ^a   Strehl, Alexander L ^a   Littman, Michael L ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

Factored representations; Hierarchical reinforcement learning; Reinforcement learning; Sample complexity

Indexed keywords

COMPUTATIONAL METHODS; HIERARCHICAL SYSTEMS; LEARNING ALGORITHMS; POLYNOMIAL APPROXIMATION; PROBLEM SOLVING; STATE SPACE METHODS;

DETERMINISTIC DOMAINS; FACTORED REPRESENTATIONS; HIERARCHICAL REINFORCEMENT LEARNING; SAMPLE COMPLEXITY;

REINFORCEMENT LEARNING;

EID: 34247204877     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1160633.1160686     Document Type: Conference Paper

References (16)

1. Craig Boutilier, Thomas Dean, and Steve Hanks. Decision-theoretic planning: Structural assumptions and computational leverage. Journal of Artificial Intelligence Research, 11:1-94, 1999.
2. Ronen I. Brafman and Moshe Tennenholtz. R-MAX - a general polynomial time algorithm for near-optimal reinforcement learning. Journal of Machine Learning Research, 3:213-231, 2002.
3. Thomas G. Dietterich. Hierarchical reinforcement learning with the MAXQ value function decomposition. Journal of Artificial Intelligence Research, 13:227-303, 2000.
4. Thomas G. Dietterich. An overview of MAXQ hierarchical reinforcement learning. In Proceedings of the Symposium on Abstraction, Reformulation and Approximation SARA 2000, pages 26-44, 2000.
5. Thomas G. Dietterich. State abstraction in MAXQ hierarchical reinforcement learning. In Advances in Neural Information Processing Systems 12, pages 994-1000, 2000.
6. Carlos Guestrin, Relu Patrascu, and Dale Schuurmans. Algorithm-directed exploration for model-based reinforcement learning in factored MDPs. In Proceedings of the International Conference on Machine Learning, pages 235-242, 2002.
7. B. Hengst. Discovering hierarchy in reinforcement learning with HEXQ. In Maching Learning: Proceedings of the Nineteenth International Conference on Machine Learning, pages 243-250. Morgan Kaufmann, 2002.
8. Leslie Pack Kaelbling. Hierarchical learning in stochastic domains: Preliminary results. In Proceedings of the Tenth International Conference on Machine Learning, Amherst, MA, 1993. Morgan Kaufmann.
9. Sham M. Kakade. On the Sample Complexity of Reinforcement Learning. PhD thesis, Gatsby Computational Neuroscience Unit, University College London, 2003.
10. Michael J. Kearns and Daphne Koller. Efficient reinforcement learning in factored MDPs. In Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI), pages 740-747, 1999.
11. Michael J. Kearns and Satinder P. Singh. Near-optimal reinforcement learning in polynomial time. Machine Learning, 49(2-3):209-232, 2002.
12. Daphne Koller and Ronald Parr. Policy iteration for factored MDPs. In Uncertainty in Artificial Intelligence, Proceedings of the Sixteenth Conference (UAI 2000), pages 326-334, 2000.
13. Andrew W. Moore and Christopher G. Atkeson. Prioritized sweeping: Reinforcement learning with less data and less real time. Machine Learning, 13:103-130, 1993.
14. Richard S. Button and Andrew G. Barto. Reinforcement Learning: An Introduction. The MIT Press, 1998.
15. Richard S. Sutton. Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In Proceedings of the Seventh International Conference on Machine Learning, pages 216-224, Austin, TX, 1990. Morgan Kaufmann.
16. Christopher J. C. H. Watkins and Peter Dayan. Q-learning. Machine Learning, 8(3):279-292, 1992.