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.

Chinese researchers have developed a system that uses passengers' brain signals to improve autonomous vehicle safety in risky ...

In a new study, Chinese researchers tested whether monitoring passengers’ brain activity could help self-driving systems make ...

This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...

ROS System, Hospital Drug Delivery Robot, Autonomous Localization, Path Planning, Navigation Simulation Cheng, B. and Zhang, B.Y. (2025) Research on Autonomous Localization and Navigation Simulation ...

You might have seen headlines sounding the alarm about the safety of an emerging technology called agentic AI.

Reinforcement learning (RL) is machine learning (ML) in which the learning system adjusts its behavior to maximize the amount of reward and minimize the amount of punishment it receives over time ...

An artificial-intelligence algorithm that discovers its own way to learn achieves state-of-the-art performance, including on some tasks it had never encountered before. Joel Lehman is at Lila Sciences ...

This GitHub repository contains the code, data, and figures for the paper FedRAIN-Lite: Federated Reinforcement Algorithms for Improving Idealised Numerical Weather and Climate Models. Also includes ...

Unmanned surface vehicles (USVs) nowadays have been widely used in ocean observation missions, helping researchers to monitor climate change, collect environmental data, and observe marine ecosystem ...

Abstract: This article proposes online data-based reinforcement learning (RL) algorithm for adaptive output consensus control of heterogeneous multiagent systems (MASs) with unknown dynamics. First, ...