Transferring Instances for Model-Based Reinforcement Learning
نویسندگان
چکیده
Reinforcement learning agents typically require a significant amount of data before performing well on complex tasks. Transfer learning methods have made progress reducing sample complexity, but they have primarily been applied to model-free learning methods, not more data-efficient model-based learning methods. This paper introduces timbrel, a novel method capable of transferring information effectively into a model-based reinforcement learning algorithm. We demonstrate that timbrel can significantly improve the sample efficiency and asymptotic performance of a model-based algorithm when learning in a continuous state space. Additionally, we conduct experiments to test the limits of timbrel’s effectiveness.
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