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- W2900553726 abstract "Reinforcement learning let agents perform repeated actions in given environments and identify how to behave in the given environments. In this paper, we showed possibilities of solutions for real-world scenarios on multiagent reinforcement learning environment. Our experiment showed an approach to investigate a way for agents to escape in an escape scenario using multiagent reinforcement learning. Two types of agents are trained. We trained Escaper Agent to find an optimal exit which increases a total number of escaped agents and decreases congestion and Exit Agent to find optimal locations for exits. Thoroughly setting reward function could simulate real-world problems and get insights on how to break down challenging questions." @default.
- W2900553726 created "2018-11-29" @default.
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- W2900553726 date "2018-10-01" @default.
- W2900553726 modified "2023-09-27" @default.
- W2900553726 title "Multiagent Reinforcement Learning in Escape Scenario" @default.
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- W2900553726 doi "https://doi.org/10.1109/ictc.2018.8539424" @default.
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