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• W2971234462 abstract "In an effort to better understand the different ways in which the discount factor affects the optimization process in reinforcement learning, we designed a set of experiments to study each effect in isolation. Our analysis reveals that the common perception that poor performance of low discount factors is caused by (too) small action-gaps requires revision. We propose an alternative hypothesis that identifies the size-difference of the action-gap across the state-space as the primary cause. We then introduce a new method that enables more homogeneous action-gaps by mapping value estimates to a logarithmic space. We prove convergence for this method under standard assumptions and demonstrate empirically that it indeed enables lower discount factors for approximate reinforcement-learning methods. This in turn allows tackling a class of reinforcement-learning problems that are challenging to solve with traditional methods." @default.
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• W2971234462 date "2019-12-01" @default.
• W2971234462 modified "2023-09-24" @default.
• W2971234462 title "Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning" @default.
• W2971234462 hasPublicationYear "2019" @default.
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