Proto-value functions: Developmental reinforcement learning

ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Volumn , Issue , 2005, Pages 553-560

  Mahadevan, Sridhar ^a  

^a Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ORTHONORMAL BASIS; PROTOVALUE FUNCTIONS; REPRESENTATION POLICY ITERATION;

FUNCTIONS; GRAPH THEORY; MATHEMATICAL MODELS; TOPOLOGY;

LEARNING SYSTEMS;

EID: 31844433360     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1102351.1102421     Document Type: Conference Paper

References (14)

1. Axler, S., Bourdon, P., & Ramey, W. (2001). Harmonic function theory. Springer.
2. Bertsekas, D. P., & Tsitsiklis, J. N. (1996). Neuro-dynamic programming. Belmont, Massachusetts: Athena Scientific.
3. Chung, F. (1997). Spectral Graph Theory. American Mathematical Society.
4. Coifman, R., & Maggioni, M. (2005). Diffusion wavelets. Applied Computational Harmonic Analysis.
5. Fowlkes, C., Belongie, S., Chung, F., & Malik, J. (2004). Spectral grouping using the Nystrom method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26, 1373-1396.
6. Frieze, A., Kannan, R., & Vempala, S. (1998). Fast Monte-Carlo algorithms for finding low-rank approximations. Proceedings of the IEEE Symposium on Foundations of Computer Science (pp. 370-378).
7. Koller, D., & Parr, R. (2000). Policy iteration for factored MDPs. Proceedings of the 16th Conference on Uncertainty in AI.
8. Lagoudakis, M., & Parr, R. (2003). Least-squares policy iteration. Journal of Machine Learning Research, 4, 1107-1149.
9. Mahadevan, S., & Maggioni, M. (2005). Value function approximation using diffusion wavelets and laplacian eigenfunctions. To be submitted.
10. Menache, I., Mannor, S., & Shimkin, N. (2002). Q-cut: Dynamic discovery of sub-goals in reinforcement learning, ECML.
11. Puterman, M. L. (1994). Markov decision processes. New York, USA: Wiley Interscience.
12. Rosenberg, S. (1997). The Laplacian on a Riemannian Manifold. Cambridge University Press.
13. Simsek, O., Wolfe, A., & Barto, A. (2005). Local graph partitioning as a basis for generating temporally extended actions in reinforcement learning. International Conference on Machine Learning.
14. Sutton, R., & Barto, A. G. (1998). An introduction to reinforcement learning. MIT Press.