ILSTD: Eligibility traces and convergence analysis

Advances in Neural Information Processing Systems

Volumn , Issue , 2007, Pages 441-448

  Geramifard, Alborz ^a   Bowling, Michael ^a   Zinkevich, Martin ^a   Sutton, Richard S ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL ADVANTAGES; CONVERGENCE ANALYSIS; ELIGIBILITY TRACES; FEATURE VECTORS; FULL COSTS; INCREMENTAL METHOD; LEAST SQUARE; LINEAR FUNCTIONS; NEW RESULTS; POLICY EVALUATION; TEMPORAL DIFFERENCE LEARNING;

LEAST SQUARES APPROXIMATIONS; REINFORCEMENT LEARNING;

APPROXIMATION ALGORITHMS;

EID: 56449115872     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. Dmitri P. Bertsekas and John N. Tsitsiklis. Neuro-Dynamic Programming. Athena Scientific, 1996.
2. Justin A. Boyan. Least-squares temporal difference learning. In Proceedings of the Sixteenth International Conference on Machine Learning, pages 49-56. Morgan Kaufmann, San Francisco, CA, 1999.
3. Justin A. Boyan. Technical update: Least-squares temporal difference learning. Machine Learning, 49:233-246, 2002.
4. S. Bradtke and A. Barto. Linear least-squares algorithms for temporal difference learning. Machine Learning, 22:33-57, 1996.
5. Alborz Geramifard, Michael Bowling, and Richard S. Sutton. Incremental least-squares temporal difference learning. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI), pages 356-361. AAAI Press, 2006.
6. RL Library. The University of Alberta reinforcement learning library. http: //rlai.cs.ualberta.ca/RLR/environment.html, 2006.
7. Peter Stone, Richard S. Sutton, and Gregory Kuhlmann. Reinforcement learning for robocup soccer keepaway. International Society for Adaptive Behavior, 13(3):165-188, 2005.
8. R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998.
9. Richard S. Sutton. Learning to predict by the methods of temporal differences. Machine Learning, 3:9-44, 1988.
10. Richard S. Sutton. Generalization in reinforcement learning: Successful examples using sparse coarse coding. In Advances in Neural Information Processing Systems 8, pages 1038-1044. The MIT Press, 1996.
11. John N. Tsitsiklis and Benjamin Van Roy. An analysis of temporaldifference learning with function approximation. IEEE Transactions on Automatic Control, 42(5):674-690, 1997.