no code implementations • 29 May 2024 • Dohyeong Kim, Taehyun Cho, Seungyub Han, Hojun Chung, Kyungjae Lee, Songhwai Oh
Furthermore, the proposed method has been evaluated on continuous control tasks and showed the best performance among other RCRL algorithms satisfying the constraints.
no code implementations • 25 Apr 2024 • Jaime Spencer, Fabio Tosi, Matteo Poggi, Ripudaman Singh Arora, Chris Russell, Simon Hadfield, Richard Bowden, Guangyuan Zhou, Zhengxin Li, Qiang Rao, Yiping Bao, Xiao Liu, Dohyeong Kim, Jinseong Kim, Myunghyun Kim, Mykola Lavreniuk, Rui Li, Qing Mao, Jiang Wu, Yu Zhu, Jinqiu Sun, Yanning Zhang, Suraj Patni, Aradhye Agarwal, Chetan Arora, Pihai Sun, Kui Jiang, Gang Wu, Jian Liu, Xianming Liu, Junjun Jiang, Xidan Zhang, Jianing Wei, Fangjun Wang, Zhiming Tan, Jiabao Wang, Albert Luginov, Muhammad Shahzad, Seyed Hosseini, Aleksander Trajcevski, James H. Elder
This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC).
no code implementations • 1 Mar 2024 • Dohyeong Kim, Mineui Hong, Jeongho Park, Songhwai Oh
This novel transformation process ensures that the converted constraints are invariant to the objective scales while having the same effect as the original objectives.
Multi-Objective Reinforcement Learning reinforcement-learning
no code implementations • 1 Dec 2023 • Dohyeong Kim, Songhwai Oh
This paper aims to solve a safe reinforcement learning (RL) problem with risk measure-based constraints.
no code implementations • 1 Dec 2023 • Dohyeong Kim, Songhwai Oh
In this paper, we propose a trust region-based safe RL method with CVaR constraints, called TRC.
1 code implementation • NeurIPS 2023 • Dohyeong Kim, Kyungjae Lee, Songhwai Oh
In safety-critical robotic tasks, potential failures must be reduced, and multiple constraints must be met, such as avoiding collisions, limiting energy consumption, and maintaining balance.
Distributional Reinforcement Learning reinforcement-learning +2
no code implementations • COLING 2022 • Dohyeong Kim, Myeongjun Jang, Deuk Sin Kwon, Eric Davis
To this end, we propose a new benchmark named Korean balanced evaluation of significant tasks (KoBEST), which consists of five Korean-language downstream tasks.