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Faster Reinforcement Learning Using Active Simulators
[article]
2017
arXiv
pre-print
In this work, we propose several online methods to build a learning curriculum from a given set of target-task-specific training tasks in order to speed up reinforcement learning (RL). These methods can decrease the total training time needed by an RL agent compared to training on the target task from scratch. Unlike traditional transfer learning, we consider creating a sequence from several training tasks in order to provide the most benefit in terms of reducing the total time to train. Our
arXiv:1703.07853v2
fatcat:qg5mqb2sejgr3mj7s6s64hicay