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• W3200659275 abstract "We present the single track road problem. In this problem two agents face each-other at opposite positions of a road that can only have one agent pass at a time. We focus on the scenario in which one agent is human, while the other is an autonomous agent. We run experiments with human subjects in a simple grid domain, which simulates the single track road problem. We show that when data is limited, building an accurate human model is very challenging, and that a reinforcement learning agent, which is based on this data, does not perform well in practice. However, we show that an agent that tries to maximize a linear combination of the human's utility and its own utility, achieves a high score, and significantly outperforms other baselines, including an agent that tries to maximize only its own utility." @default.
• W3200659275 created "2021-09-27" @default.
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• W3200659275 creator A5082567694 @default.
• W3200659275 date "2021-09-12" @default.
• W3200659275 modified "2023-09-26" @default.
• W3200659275 title "A Socially Aware Reinforcement Learning Agent for The Single Track Road Problem" @default.
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• W3200659275 doi "https://doi.org/10.48550/arxiv.2109.05486" @default.
• W3200659275 hasPublicationYear "2021" @default.
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