### Model-based RL1.**Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion** NIPS2018. [paper](https://arxiv.org/abs/1807.01675) *Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee*2.**Model-Based Value Estimation for Efficient Model-Free Reinforcement Learning** ICML2018.[paper](https://arxiv.org/abs/1803.00101) *Vladimir Feinberg, Alvin Wan, Ion Stoica, Michael I. Jordan, Joseph E. Gonzalez, Sergey Levine* 3.**Value Prediction Network** NIPS2017. [paper](https://arxiv.org/abs/1707.03497) *Vladimir Feinberg, Alvin Wan, Ion Stoica, Michael I. Jordan, Joseph E. Gonzalez, Sergey Levine*4.**Imagination-Augmented Agents for Deep Reinforcement Learning** NIPS2017. [paper](https://arxiv.org/abs/1707.06203) *Théophane Weber, Sébastien Racanière, David P. Reichert, Lars Buesing, Arthur Guez, Danilo Jimenez Rezende, Adria Puigdomènech Badia, Oriol Vinyals, Nicolas Heess, Yujia Li, Razvan Pascanu, Peter Battaglia, Demis Hassabis, David Silver, Daan Wierstra*5.**Continuous Deep Q-Learning with Model-based Acceleration** ICML2016. [paper](https://arxiv.org/abs/1603.00748)*Shixiang Gu, Timothy Lillicrap, Ilya Sutskever, Sergey Levine*6.**Uncertainty-driven Imagination for Continuous Deep Reinforcement Learning** CoRL2017. [paper](http://proceedings.mlr.press/v78/kalweit17a/kalweit17a.pdf)*Gabriel Kalweit, Joschka Boedecker*7.**Model-Ensemble Trust-Region Policy Optimization** ICLR2018. [paper](https://arxiv.org/abs/1802.10592)*Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel*
