Optimizing Betting Fraction in Compound Reinforcement Learning

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  • 複利型強化学習における投資比率の最適化

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This paper describes optimization of the betting fraction parameter in compound reinforcement learning. Compound reinforcement learning maximizes the expected logarithm of compound returns in return-based MDPs. However, a new betting fraction parameter is introduced in order not to diverge values to negative infinity and it causes a problem of choosing the parameter. In this paper, we proposed a method to optimize the betting fraction with on-line gradient ascent in compound reinforcement learning.

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