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• W265131677 abstract "Crowd Simulation is an area of research that is present in several disciplines andindustries. Even though the visualization of crowds is an important subject, thebehavior behind it helps to make it believable. Behavioral modeling can be a te-dious task because the agents have to mimic the complexity of human reactions tosituations. In this work, we propose an alternative model for crowd simulation ofpedestrian movements using Reinforcement Learning methods for low level deci-sions. Taking the approach of microscopic models, we trained an agent to reach agoal point while avoiding obstacles that might be on its way, and trying to follow acoherent path during the walk. Once one agent has learned, its knowledge is passedto the rest of the members of the crowd by sharing the resulting Q-Table, expectingthe individual behavior and interactions to lead to a crowd behavior. We presentedstates sets, an action set and reward functions general enough to adapt to differentenvironments, allowing us to use the same knowledge in different scenario settings." @default.
• W265131677 created "2016-06-24" @default.
• W265131677 creator A5072303246 @default.
• W265131677 date "2014-07-24" @default.
• W265131677 modified "2023-10-16" @default.
• W265131677 title "Social crowd controllers using reinforcement learning methods" @default.
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• W265131677 hasPublicationYear "2014" @default.
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