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 ...

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

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

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 ...