Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

NeurIPS 2018 Jacob BuckmanDanijar HafnerGeorge TuckerEugene BrevdoHonglak Lee

Integrating model-free and model-based approaches in reinforcement learning has the potential to achieve the high performance of model-free algorithms with low sample complexity. However, this is difficult because an imperfect dynamics model can degrade the performance of the learning algorithm, and in sufficiently complex environments, the dynamics model will almost always be imperfect... (read more)

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