reinforcement learning

Temporal coordination aware reinforcement learning for multi-agent UAV navigation in dynamic environments

T-CARE is introduced, a hybrid learning-heuristic multi-UAV coordination framework that integrates zero-shot constrained action selection with priority-aware temporal reservations and achieves 100% success, 0% collision rate, and no observed persistent starvation or deadlock.

Test-Time Weak-to-Strong Alignment: Transferring Implicit Rewards from Weak to Strong Flow Models

The method, AlignGraft, aligns a larger, frozen, never-tuned model by adding the pair's velocity difference during sampling by adding the pair's velocity difference during sampling, and preserves the large model's fidelity at a small constant sampling overhead.

SCoCaT: Success Conditioned Constrained Reinforcement Learning for Spacecraft Docking

This work identifies a structural failure mode of this method class on terminal-navigation tasks: reaching a precise goal configuration while satisfying safety constraints that tighten along the final approach, and empirically demonstrates that adding a dense per-step success signal via an auxiliary value critic improv...

Estrategias pedagógicas para potenciar el proceso de enseñanza - aprendizaje de niños con Trastorno del Espectro Autista

The teaching-learning process for children with autism spectrum disorder requires structured and context-specific strategies. The objective was to propose pedagogical interventions to strengthen this process at the “América” Educational Unit. An applied, descriptive-propositional study was conducted using a mixed-metho...

Event-Triggered Preference Adaptation with Large Language Model-Guided Conditional Reinforcement Learning for Pedestrian-Aware Traffic Signal Control

An event-triggered large language model-guided conditional reinforcement learning framework that provides stronger adaptability and more interpretable multi-objective decision-making for pedestrian-aware traffic signal control is proposed.

Peran Guru Akidah Akhlak dalam Menanamkan Digital Ethics pada Gen Z

The impact of the role of Akidah Akhlak teachers in instilling Digital Ethics could be seen in the improvement of students’ moral knowing, moral feeling, and moral action in using digital media more ethically and responsibly.

Artificial Intelligence in Scalable Materials Synthesis and Manufacturing

The article concludes with a forward-looking roadmap for the future of AI in chemical and materials engineering, defined by a paradigm shift from simply finding new materials to creating viable, economical and scalable pathways to produce them, thereby enabling a new era of synthesis-aware materials innovation.

Multi-Agent RL-Based Dynamic Feeder Acceptance Orchestration Using the FeederBW Dataset and 236-Bus Low-Voltage Distribution Network.

This study proposes a Dynamic Orchestration Framework for Low-Voltage Feeder Acceptance based on intelligent-agent collaboration within a multi-agent reinforcement learning approach that achieves improved adaptability, operational resilience, and decentralized decision-making efficiency under the simulated low-voltage...

Assembling Two Parts in One Hand

A reinforcement learning formulation is presented to solve in-hand assembly: mating two rigid objects within a single dexterous hand, with no second arm and no fixture, which shows robustness to state-estimation errors caused by oc- clusion.

A Risk-Sensitive and Uncertainty-Aware Decision-Making and Control Framework for Safe and Robust Autonomous Driving

A Risk-sensitive and Uncertainty-aware Decision-making and Control (RUDC) framework for safe and robust autonomous driving that couples risk-sensitive distributional RL with ensemble-based policy uncertainty quantification, jointly accounting for tail risks in return distributions and uncertainty in learned policies.

Physics-informed reinforcement learning for energy-efficient unmanned aerial vehicle navigation with context-aware power estimation

Model-assisted multi-agent reinforcement learning for collaborative synchromodal transport re-planning under service time uncertainty