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

no code implementations • NeurIPS 2021 • Anish Agarwal, Abdullah Alomar, Varkey Alumootil, Devavrat Shah, Dennis Shen, Zhi Xu, Cindy Yang

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 sub-optimal policy.

Offline RL reinforcement-learning +1