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- W23172241 abstract "We discuss the reinforcement learning from an intertemporal choice perspective. Different from previous research, this paper wants to emphasize the importance of deeper understanding the psychological mechanism of human decision-making. In what follows we aim to improve the previous Q learning algorithm according to the new results of intertemporal choice experiments. We start with a brief introduction to new findings of intertemporal choice theory and reinforcement learning. Then we propose a new reinforcement learning algorithm with selective discount (SD-Q). Experiments show that, SD-Q is superior to both the traditional Q learning algorithm and the reinforcement learning method without considering the discount." @default.
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- W23172241 date "2011-01-01" @default.
- W23172241 modified "2023-09-25" @default.
- W23172241 title "SD-Q: Selective Discount Q Learning Based on New Results of Intertemporal Choice Theory" @default.
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- W23172241 doi "https://doi.org/10.1007/978-3-642-23887-1_88" @default.
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