reinforcement learning

A comprehensive review of reinforcement learning approaches in peg-in-hole insertion for robotic assembly tasks

Contributions of Positive Social Interactions to Vocal Learning and Social Cohesion

Different contributions of opioid and dopaminergic signaling to the production, reinforcement, and fine‐tuning of species‐specific songs are discussed, and ways in which reward associated with song production outside the breeding context contributes to flock cohesion are highlighted.

EdgeTwin-DRL: Real-Time Counter-UAS Detection and Response Optimization Using Edge-Assisted Digital Twins and Multi-Agent Deep Reinforcement Learning

The comparative performance of the proposed EdgeTwin-DRL architecture within the simulated environment is demonstrated and motivated to motivate further investigation of edge-assisted digital twins for counter-drone decision support.

A bibliometric analysis of research on the application of just-in-time adaptive interventions in mental health

The application of JITAIs in mental health is developing rapidly and has become a hot topic in interdisciplinary research, however, research capacity is unevenly distributed, and international collaboration needs strengthening.

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...