Abstract
An agent that must learn to act in the world by trial and error faces the reinforcement learning problem, which is quite different from standard concept learning. Although good algorithms exist for this problem in the general case, they are often quite inefficient and do not exhibit generalization. One strategy is to find restricted classes of action policies that can be learned more efficiently. This paper pursues that strategy by developing an algorithm that performans an on-line search through the space of action mappings, expressed as Boolean formulae. The algorithm is compared with existing methods in empirical trials and is shown to have very good performance.
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Duda, R. O., Gaschnig, J., & Hart, P. E. (1979). Model design in the Prospector consultant system for mineral exploration. In D. Michie (Ed.), Expert Systems in the Micro Electronic Age. Edinburgh, U.K.: Edinburgh University Press.
Duda, R. O., Hart, P. E., & Nilsson, N. J. (1976). Subjective Bayesian Methods for Rule-Based Inference Systems. Technical Report 124, Artificial Intelligence Center, SRI International, Menlo Park, California.
Kaelbling, L. P. (1994). Associative reinforcement learning: Functions in κ-DNF. Machine Learning, 15, 279–298.
Kaelbling, L. P. (1993). Learning in Embedded Systems. Cambridge, Massachusetts: The MIT Press. Also available as a PhD Thesis from Stanford University, 1990.
Saxena, S. (1991). Predicting the Effect of Instance Representations on Inductive Learning. PhD thesis, University of Massachusetts, Amherst, Massachusetts.
Schlimmer, J. C. (1987). Concept Acquisition Through Representational Adjustment. PhD thesis, University of California, Irvine, Irvine, California.
Schlimmer, J. C. & Granger, Jr., R. H. (1986). Incremental learning from noisy data. Machine Learning, 1 (3), 317–354.
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Kaelbling, L.P. Associative Reinforcement Learning: A Generate and Test Algorithm. Machine Learning 15, 299–319 (1994). https://doi.org/10.1023/A:1022642026684
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DOI: https://doi.org/10.1023/A:1022642026684