Reinforcement　Learning　with　Orthonormal　Basis　Adaptation　Based　on　Activity-Oriented　Index　Allocation

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概要

• 論文の詳細を見る

• An orthonormal basis adaptation method for function approximation was developed and applied to reinforcement learning with multi-dimensional continuous state space. First, a basis used for linear function approximation of a control function is set to an orthonormal basis. Next, basis elements with small activities are replaced with other candidate elements as learning progresses. As this replacement is repeated, the number of basis elements with large activities increases. Example chaos control problems for multiple logistic maps were solved, demonstrating that the method for adapting an orthonormal basis can modify a basis while holding the orthonormality in accordance with changes in the environment to improve the performance of reinforcement learning and to eliminate the adverse effects of redundant noisy states.

• 電子情報通信学会の論文
• 2008-04-01

著者

• 佐藤 仁樹

  Future University-Hakodate

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• A　State　Space　Compression　Method　Based　on　Multivariate　Analysis　for　Reinforcement　Learning　in　High-dimensional　Continuous　State　Spaces
• Reinforcement　Learning　with　Orthonormal　Basis　Adaptation　Based　on　Activity-Oriented　Index　Allocation

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