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

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 ...

Researchers at the University of Science and Technology of China have developed a new reinforcement learning (RL) framework that helps train large language models (LLMs) for complex agentic tasks ...

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 ...

Download PDF Join the Discussion View in the ACM Digital Library Deep reinforcement learning (DRL) has elevated RL to complex environments by employing neural network representations of policies. 1 It ...

The change is part of a deal to bring TikTok under U.S. ownership to avert a looming ban. By Emmett Lindner and Lauren Hirsch The software giant Oracle will oversee the security of Americans’ data and ...

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 ...

Reinforcement learning (RL) plays a crucial role in scaling language models, enabling them to solve complex tasks such as competition-level mathematics and programming through deeper reasoning.

Abstract: Data-driven inverse reinforcement learning (RL) control aims to infer the unknown cost function of a learner system from expert demonstrations. The convergence of existing methods ...

Abstract: In order to apply Internet of Things technologies into real world, the low-power low-cost protocol stack CoAP/UDP/IPv6/RPL/6LoWPAN/802.15.4-802.15.4e is a ...