This system utilizes machine learning algorithms to optimize the operation of particle accelerators, reducing manual intervention and enhancing precision in real-time control. By integrating virtual ...

Keeping high-power particle accelerators at peak performance requires advanced and precise control systems. For example, the primary research machine at the U.S. Department of Energy's Thomas ...

From autonomous cars to video games, reinforcement learning (machine learning through interaction with environments) can have an important impact. That may feel especially true, for example, when ...

This paper is about how robots (in particular, household robots like mobile manipulators) can autonomously acquire skills via ...

Microgrids play a growing role in modern power systems, supporting renewable integration, local resilience, and decentralized ...

Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive optical networks, in particular, enable large-scale parallel computation ...

The release comes as governments and enterprises face growing constraints on power availability, environmental impact, and data control associated with large AI data centers. As A ...

UC Santa Cruz’s group of researchers were the winning team of the L2RPN Delft 2023, a competition which invited participants from around the world to use reinforcement learning or similar techniques ...

Reinforcement Learning from Human Feedback (RLHF) has emerged as a crucial technique for enhancing the performance and alignment of AI systems, particularly large language models (LLMs). By ...

Let’s look at how RL agents are trained to deal with ambiguity, and it may provide a blueprint of leadership lessons to ...