Reinforcement learning (RL) algorithms can achieve state-of-the-art performance in decision- making and continuous control tasks. However, applying RL algorithms on safety-critical …
X Hu, P Chen, Y Wen, B Tang, L Chen - arXiv preprint arXiv:2403.18209, 2024 - arxiv.org
Reinforcement learning (RL) has been widely used in decision-making tasks, but it cannot guarantee the agent's safety in the training process due to the requirements of interaction …
W Wang, X Zhou, B Xu, M Lu… - 2022 41st Chinese …, 2022 - ieeexplore.ieee.org
Reinforcement learning (RL) holds the promise of autonomous robots because it can adapt to dynamic or unknown environments by automatically learning optimal control policies from …
Reinforcement Learning (RL) has achieved tremendous success in many complex decision- making tasks. However, safety concerns are raised during deploying RL in real-world …
D Kim, Y Kim, K Lee, S Oh - 2022 IEEE/RSJ International …, 2022 - ieeexplore.ieee.org
In reinforcement learning (RL), exploration is essential to achieve a globally optimal policy but unconstrained exploration can cause damages to robots and nearby people. To handle …
Autonomous agents are playing an ever-increasing role, including in automated driving vehicles or house-care robots. When an autonomous agent interacts with humans, one of …
X Zhang, Y Peng, B Luo, W Pan, X Xu, H Xie - arXiv preprint arXiv …, 2021 - arxiv.org
Recently, safe reinforcement learning (RL) with the actor-critic structure for continuous control tasks has received increasing attention. It is still challenging to learn a near-optimal …
Reinforcement learning (RL) has become a prominent research area in recent years due to its ability to address decision-making problems by maximizing cumulative rewards in …
X Wang - IEEE Robotics and Automation Letters, 2022 - ieeexplore.ieee.org
Reinforcement learning (RL) has been successfully applied to sequential decision-making problems, eg, playing computer games or solving robotic tasks in simulations. However, RL …