Risk-Aware Model-Based Control
Model-Based Reinforcement Learning (MBRL) algorithms have been shown to have an advantage on data-efficiency, but often overshadowed by state-of-the-art model-free methods in performance, especially when facing high-dimensional and complex problems. In this work, a novel MBRL method is proposed, cal...
Main Authors: | Chen Yu, Andre Rosendo |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2021-03-01
|
Series: | Frontiers in Robotics and AI |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2021.617839/full |
Similar Items
-
Risk-Aware Deep Reinforcement Learning for Robot Crowd Navigation
by: Xueying Sun, et al.
Published: (2023-11-01) -
Risk-aware multi-armed bandit problem with application to portfolio selection
by: Xiaoguang Huo, et al.
Published: (2017-01-01) -
Multi-Stage Volt/VAR Support in Distribution Grids: Risk-Aware Scheduling With Real-Time Reinforcement Learning Control
by: Mohammad Mansourlakouraj, et al.
Published: (2023-01-01) -
PSTO: Learning Energy-Efficient Locomotion for Quadruped Robots
by: Wangshu Zhu, et al.
Published: (2022-03-01) -
Energy risk measurement and hedging analysis by nonparametric conditional value at risk model
by: Ling Li, et al.
Published: (2022-08-01)