COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL

no code implementations • 11 Oct 2023 • Xiyao Wang, Ruijie Zheng, Yanchao Sun, Ruonan Jia, Wichayaporn Wongkamjan, Huazhe Xu, Furong Huang

In this paper, we propose $\texttt{COPlanner}$, a planning-driven framework for model-based methods to address the inaccurately learned dynamics model problem with conservative model rollouts and optimistic environment exploration.

Continuous Control Model-based Reinforcement Learning +1

Theoretically Guaranteed Policy Improvement Distilled from Model-Based Planning

no code implementations • 24 Jul 2023 • Chuming Li, Ruonan Jia, Jie Liu, Yinmin Zhang, Yazhe Niu, Yaodong Yang, Yu Liu, Wanli Ouyang

Model-based reinforcement learning (RL) has demonstrated remarkable successes on a range of continuous control tasks due to its high sample efficiency.

Continuous Control Model-based Reinforcement Learning +1