Abstract. 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.
In contrast to previous work, which were mostly based on model-free methods, Taylor et al. REF propose a transfer-learning method for a model-based reinforcement learning algorithm for continuous state space.
Transferring instances for model-based reinforcement learning
AAMAS 2008 Workshop on Adaptive Learning Agents and Multi-Agent Systems
Computer Science