The review introduces a proposed two-layer reinforcement learning framework for distributed smart grid control. In this architecture, upper-layer agents manage long-term global optimization tasks, ...

Reinforcement learning designs intelligent agents by training them to maximize rewards as they interact with their environment. As a machine learning researcher, I find it fitting that reinforcement ...

Agentic AI has emerged as the latest permutation of generative AI, enabling autonomous functionality as a way to deliver business value. But how the agents work can be a bit of a mystery.

Reinforcement Learning (RL) is a type of machine learning where a model learns to make decisions by interacting with an environment. Unlike supervised learning, where the model is provided with ...

There has been much talk about how AI could recursively self-improve in the coming years, but it appears that Google ...

AI agents develop their own communication channels beyond our monitoring frameworks, we face a pivotal challenge: harnessing ...

In the ever-evolving world of artificial intelligence (AI), the ability to make effective decisions is a cornerstone of ...

By categorizing and filtering user input, you can better focus on driving AI improvement. This iterative process—blending automation with human review—ensures AI learns from high-quality data, leading ...

Enhancing Microsoft CyberBattleSim for Enterprise Cybersecurity Simulations. Journal of Information Security, 16, 270-282. doi: 10.4236/jis.2025.162014 . Quantifying the effectiveness of cyber defense ...