Why reinforcement learning plateaus without representation depth (and other key takeaways from NeurIPS 2025) ...

Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive ...

Request To Download Free Sample of This Strategic Report @- The global reinforcement learning market is experiencing a period of rapid growth, with revenue estimated to increase from approximately $3 ...

In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...

Imagine trying to teach a child how to solve a tricky math problem. You might start by showing them examples, guiding them step by step, and encouraging them to think critically about their approach.

FPMCO decomposes multi-constraint RL into KL-projection sub-problems, achieving higher reward with lower computing than second-order rivals on the new SCIG robotics benchmark.

What if the very techniques we rely on to make AI smarter are actually holding it back? A new study has sent shockwaves through the AI community by challenging the long-held belief that reinforcement ...

Traffic congestion, fuel consumption, and emissions also offer quantifiable performance indicators, making mobility uniquely ...

Machine learning is reshaping the way portfolios are built, monitored, and adjusted. Investors are no longer limited to ...