Oct 11, 2023 · A planning-driven framework for model-based methods to address the inaccurately learned dynamics model problem with conservative model rollouts and optimistic ...
Nov 19, 2023 · In this paper, we propose COPlanner , a planning-driven framework for model-based methods to address the inaccurately learned dynamics model problem with ...
A powerful planning-driven framework for model-based Reinforcement Learning to mitigate the impact of imperfect world models and boosting policy learning.
In this paper, we propose COPlanner, a planning-driven framework for model-based methods to address the inaccurately learned dynamics model problem with ...
Figure 2: COPlanner Framework. The most essential part of COPlanner is the Uncertainty-aware Policy-. Guided MPC (UP-MPC) phase in which we plan ...
Jan 24, 2024 · Year: 2024; Type(s): Conference proceedings; Author(s): Wang, Xiyao, Ruijie Zheng, Yanchao Sun, Ruonan Jia, Wichayaporn Wongkamjan, ...
Oct 11, 2023 · In this paper, we propose$\texttt{COPlanner}$, a planning-driven framework for model-based methods toaddress the inaccurately learned dynamics ...
COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL. ICLR 2024 Poster. Xiyao Wang · Ruijie Zheng · Yanchao Sun ...
Jan 22, 2024 · Introducing COPlanner: the Game Changer in Model-Based #RL ... Roll Out Conservatively but to Explore Optimistically for Model-Based RL" A.
2024. COPlanner: Plan to roll out conservatively but to explore optimistically for model-based rl. X Wang, R Zheng, Y Sun, R Jia, W Wongkamjan, H Xu, F Huang.