Abstract: This paper proposes a hierarchical safe reinforcement learning with prescribed performance control (HSRL-PPC) scheme to address the challenges of interconnected leader-follower systems ...

From deep reinforcement learning to advanced gyroscopic systems, AI is revolutionizing how spacecraft orient themselves in space. New algorithms can handle complex constraints, adapt to unknown ...

The technique, called Reinforcement Learning with Verifiable Rewards with Self-Distillation (RLSD), combines the reliable ...

Control systems shape the performance of technologies from autonomous vehicles to power plants. With MATLAB and Simulink, you can virtually model, design, and optimize controllers before deploying ...

Abstract: Integrating learning-based techniques, especially reinforcement learning, into robotics is promising for solving complex problems in unstructured environments. Most of the existing ...

Reinforcement learning has become the central approach for language models (LMs) to learn from environmental reward or feedback. In practice, the environmental feedback is usually sparse and delayed.

In reinforcement learning (RL), an agent learns to achieve its goal by interacting with its environment and learning from feedback about its successes and failures. This feedback is typically encoded ...

Over the past few years, AI systems have become much better at discerning images, generating language, and performing tasks within physical and virtual environments. Yet they still fail in ways that ...

Every year, NeurIPS produces hundreds of impressive papers, and a handful that subtly reset how practitioners think about scaling, evaluation and system design. In 2025, the most consequential works ...

This repository showcases a hybrid control system combining Reinforcement Learning (Q-Learning) and Neural-Fuzzy Systems to dynamically tune a PID controller for an Autonomous Underwater Vehicle (AUV) ...