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PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators [article]

Anish Agarwal, Abdullah Alomar, Varkey Alumootil, Devavrat Shah, Dennis Shen, Zhi Xu, Cindy Yang
2021 arXiv   pre-print
We consider offline reinforcement learning (RL) with heterogeneous agents under severe data scarcity, i.e., we only observe a single historical trajectory for every agent under an unknown, potentially  ...  To address this challenge, we propose PerSim, a model-based offline RL approach which first learns a personalized simulator for each agent by collectively using the historical trajectories across all agents  ...  of Reinforcement Learning, and scholarship from KACST (for Abdullah Alomar).  ... 
arXiv:2102.06961v4 fatcat:uprepkqw6zfrvkxc72aufdmc7y