Tech Xplore

Reinforcement learning accelerates model-free training of optical AI systems

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

True agentic AI is years away - here's why and how we get there

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.
IEEE

Synthesizing Control Barrier Functions With Artificial Potential Fields for Safe Reinforcement Learning

Abstract: In complex and dynamic environments, achieving autonomous decision-making and control of agent remains a challenging task. Traditional reinforcement learning algorithms often struggle to ...
eLife

A differentiable model for optimizing the genetic drivers of synaptogenesis

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

Deep Reinforcement Learning with Graph Neural Networks: An Indepth Analysis of Algorithms and Applications

Abstract: Deep Reinforcement Learning (DRL) enable several areas of artificial intelligence, including perception recognition, expert system, recommender program and game. Also, graph neural networks ...
Microsoft

Agent Lightning: Adding reinforcement learning to AI agents without code rewrites

AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...