Using Transitional Proximity for Faster Reinforcement Learning

Proceedings of the 9th International Workshop on Machine Learning, ICML 1992

Volumn , Issue , 1992, Pages 316-321

  McCallum, R Andrew ^a  

^a UNIVERSITY OF ROCHESTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

'CURRENT; CURRENT PATHS; FAST LEARNING; INTERNAL STATE; KOHONEN NETWORK; LEARN+; LEARNING NETWORK; OPTIMAL POLICIES; Q-LEARNING; REINFORCEMENT LEARNINGS;

REINFORCEMENT LEARNING;

EID: 84957622922     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/B978-1-55860-247-2.50045-0     Document Type: Conference Paper

References (12)

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3. [Chapman, 1990] David Chapman. Vision, Instruction, and action. PhD thesis, MIT Artificial Intelligence Laboratory, 1990.
4. [Hertz et al., l#9l] John Hertz, Anders Krogh, and Richard Palmer. Introduction to the Theory of Neural Computation. Addison-Wesley, Redwood City, California, 1991.
5. [Kohonen, 1989] Teuvo Kohonen. Self-Organization and Associative Memory (3rd ed.). Springer-Verlag, 1989.
6. [Lin, 1991] Long-Ji Lin. Programming robots using reinforcement learning and teaching. AAAI, 1991.
7. [Martinetz and Schulten, 1991] T. Martinetz and K. Schulten. A "neural gas" network for vector quantization and learning of unknown topologies, (unpub.) Beckman Institute, UI-UC, 1991.
8. [McCallum, 1992] R. Andrew McCallum. Using transitional proximity for faster reinforcement learning. Technical report, Department of Computer Science, University of Rochester, 1992.
9. [Sutton, 1990] Richard S. Sutton. First results with DYNA, an integrated architecture for learning, planning, and reacting. In Proceedings of the AAAI Spring Symposium on Planning in Uncertain, Unpredictable, or Changing Environments, 1990.
10. [Tan, 1991] Ming Tan. Cost sensitive reinforcement learning for adaptive classification and control. In AAAI, 1991.
11. [Watkins, 1989] Chris Watkins. Learning from delayed rewards. PhD thesis, Cambridge University, 1989.
12. [Whitehead, 1992] Steven Whitehead. Reinforcement Learning for the Adaptive Control of Perception and Action. PhD thesis, Department of Computer Science, University of Rochester, 1992.