Tag: Reinforcement Learning

A multi-objective reinforcement-learning system reduced cobot trajectory error, collisions, cycle time, and energy use in a controlled simulation benchmark, while ...

A new cloud-edge framework transmits only a losslessly compressed ControlNet condition instead of the raw reference image, cutting uplink payloads ...

A new open-access cross-domain review maps how the same AI algorithms recur across IoT security, network routing, computer vision, and ...

A new MARL framework guided by MPC expert knowledge achieves coordinated frequency control across interconnected microgrids, with pass rates up ...

A new hybrid Pareto-safe distributional reinforcement learning scheduler balances latency, energy, SLA, and load balance in cloud task allocation while ...

A new review maps how advances in computer vision, vision language models and reinforcement learning could enable collaborative robots to ...

Researchers trained a deep reinforcement learning agent to plan collision-free paths for a fuel-saving free-floating space manipulator, even with noisy ...

A new review argues that human-centric digital twins must deliver measurable benefits to factory workers in safety, ergonomics and wellbeing, ...

A new perspective in Frontiers in Neurorobotics proposes that humanoid robots could unlock embodiment-dependent neural signals, such as touch and ...

The architecture of cloud radio access networks fundamentally reshapes where computational intelligence lives in a cellular system. In a conventional ...

A comprehensive new review traces how diffusion models, the generative engines behind modern image synthesis, have become the dominant force ...

Researchers at the University of Ottawa have developed a dual-agent reinforcement learning framework that values soccer actions by modeling both ...

Researchers embedded Hilbert space-filling curve priors into reinforcement learning, boosting multi-robot ecological survey coverage from 0.589 to 0.895 with DQN ...

Reinforcement learning and swarm intelligence combine to let cloud systems re-provision resources on the fly as workflows change mid-execution, outperforming ...

A systematic review of thirteen studies shows that brain signals called error-related potentials can be decoded to help robots detect ...

A University of Manchester team has built an AI workflow that combines stakeholder weights, Bayesian deep learning and reinforcement learning ...

Researchers have combined YOLOv12 detection, capsule networks, reinforcement learning, and Harris Hawks Optimization into a real-time face detection framework that ...

Researchers have built a deep reinforcement learning agent that dynamically tunes feedback parameters to help large expert groups reach consensus ...

Researchers at Zhejiang University developed a two-stage training framework that uses temporary virtual forces to help humanoid robots learn highly ...

Researchers have trained a reinforcement learning system that autonomously plans collisions between robots to determine whether a malfunctioning agent's fault ...