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Learning, Exploration And Chaotic Policies

Author

Listed:
  • ALEXEI B. POTAPOV

    (Department of Physics, The University of Lethbridge, 4401 University Dr. W Lethbridge, Alberta T1K 3M4, Canada)

  • M. K. ALI

    (Department of Physics, The University of Lethbridge, 4401 University Dr. W Lethbridge, Alberta T1K 3M4, Canada)

Abstract

We consider different versions of exploration in reinforcement learning. For the test problem, we use navigation in a shortcut maze. It is shown that chaotic ∊-greedy policy may be as efficient as a random one. The best results were obtained with a model chaotic neuron. Therefore, exploration strategy can be implemented in a deterministic learning system such as a neural network.

Suggested Citation

  • Alexei B. Potapov & M. K. Ali, 2000. "Learning, Exploration And Chaotic Policies," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 11(07), pages 1455-1464.
    • Handle: RePEc:wsi:ijmpcx:v:11:y:2000:i:07:n:s0129183100001309
    DOI: 10.1142/S0129183100001309
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