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

Most current autonomous driving systems rely on single-agent deep learning models or end-to-end neural networks. While ...

This article explores the five biggest mistakes leaders will make with AI agents, from data and security failures to human ...

VoAgents’ self-learning voice AI technology enables 24/7 customer engagement, lead conversion, and operational efficiency across industries. Imagine having your best employee available on every phone ...

Abstract: The increasing demand for adaptive and autonomous smart ocean systems has accelerated the deployment of Autonomous Underwater Vehicles (AUVs) in various complex underwater missions, ...

At HIMSS26, Dr. Nathan Moore of the BJC Accountable Care Organization will show how health systems can move beyond chatbots ...

For a minimal example of how to use the environment framework, refer to examples/simple-calculator. For the environment and training data used in our paper, see AgentBench FC. For reproducing the ...

Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.

As AI moves from controlled experiments into real-world applications, we are entering an inflection point in the security ...

See how Langraph powers a multi-agent stock sim with configurable rounds and models, helping you compare trade plans without ...

Abstract: In recent years, rapid urbanization has led to increased traffic congestion, rendering traditional traffic light control methods ineffective. Deep Reinforcement Learning (DRL) has emerged as ...