Automatic basis function construction for approximate dynamic programming and reinforcement learning

ACM International Conference Proceeding Series

Volumn 148, Issue , 2006, Pages 449-456

  Keller, Philipp W ^a   Mannor, Shie ^a   Precup, Doina ^a  

^a MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

INVENTORY CONTROL; MARKOV PROCESSES; REINFORCEMENT LEARNING;

BELLMAN ERROR; BERTSEKAS AND CASTAFION; DIMENSIONALITY REDUCTION TECHNIQUE; LINEAR APPROXIMATION; NEIGHBORHOOD COMPONENT ANALYSIS; TEMPORAL DIFFERENCE; VALUE ITERATION;

DYNAMIC PROGRAMMING;

EID: 34250706852     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1143844.1143901     Document Type: Conference Paper

References (17)

1. Bertsekas, D. (2005). Dynamic programming and optimal control vol. 1 third edition. Athena Scientific.
2. Bertsekas, D., & Castañon, D. (1989). Adaptive aggregation methods for infinite horizon dynamic programming. IEEE Transactions on Automatic Control, 34, 589-598.
3. Boyan, J. (2002). Technical update: Least-squares temporal difference learning. Machine Learning.
4. Bradtke, S., & Barto, A. (1996). Linear least-squares algorithms for temporal difference learning. Machine Learning, 22, 33-57.
5. Fradkin, D., & Madigan, D. (2003). Experiments with random projections for machine learning. In Proc. of KDD.
6. Goldberger, J., Roweis, S., Hinton, G., & Salakhutdinov, R. (2005). Neighbourhood components analysis. In NIPS 17, 513-520.
7. Jacobs, R. A. (1988). Increased rates of convergence through learning rate adaptation. Neural Networks, 1, 295-307.
8. Mahadevan, S. (2005). Samuel meets Amarel: Automating value function approximation using global state space analysis. In Proceedings of AAAI.
9. Mannor, S., Menache, I., & Shimkin, N. (2005). Basis function adaptation in temporal difference reinforcement learning. Annals of Operations Research, 134, 215-238.
10. Munos, R., & Moore, A. (2002). Variable resolution discretization in optimal control. Machine Learning, 49, 291-323.
11. Ratitch, B., & Precup, D. (2004). Sparse distributed memories for on-line value-based reinforcement learning. In Proceedings of ECML.
12. Smart, W. (2004). Explicit manifold representations for value-functions in reinforcement learning. In Proceedings of the 8th int. symp. on ai and mathematics.
13. Sutton, R. (1988). Learning to predict by the methods of temporal differences. Machine Learning, 3, 9-44.
14. Sutton, R., & Barto, A. (1998). Reinforcement learning: An introduction. MIT Press.
15. Tadic, V. (2001). On the convergence of temporal-difference learning with linear function approximation. Machine learning, 42, 241-267.
16. Tsitsiklis, J., & Van Roy, B. (1997). An analysis of temporal-difference learning with function approximation. IEEE Transactions on Automatic Control, 42, 674-690.
17. Ziv, O., & Shimkin, N. (2005). Multigrid algorithms for temporal difference reinforcement learning. In Proc. icml workshop on rich representations for rl.