reinforcement learning

Reinforcement Learning-Based AI Framework for Decision-Making and Path Optimization in Swarm Robots

A safety aware framework for conversational health agents integrating fuzzy logic reinforcement learning and large language models with self determination theory and constrained optimization called SAFE CHA

Гібридний multi-agent deep reinforcement learning підхід до динамічного розподілу завдань і планування маршрутів AGV/AMR у складському господарстві транспортного підприємства

Запропоновано гібридний метод динамічного розподілу завдань і планування маршрутів для флоту автоматизованих керованих транспортних засобів (AGV) та автономних мобільних роботів (AMR) у складському господарстві транспортного підприємства. Метод поєднує cooperative multi-agent deep reinforcement learning (MADRL) для спі...

Performance Analysis of Ensemble and Hybrid Deep Learning Models in Predicting Agricultural Chemical Residues and Crop Outcome

The contemporary agricultural system has substantially transformed its productivity through extensive deployment of chemical pesticides, herbicides, and fertilizers. However, these practices introduce significant environmental sustainability concerns and human health risks. This paper conducts a comprehensive performan...

Globally guided multi-agent reinforcement learning with decoupled critics for cooperative UAV path planning

Abstract Cooperative path planning for multiple unmanned aerial vehicles (UAVs) requires the simultaneous achievement of target navigation, collision avoidance, and formation coordination in complex obstacle environments. Existing multi-agent reinforcement learning methods often suffer from sparse rewards, local deadlo...

Hierarchical multi-fault diagnosis of IPMSM drives using speed-normalized wavelet features and random forests

Purpose Interior permanent magnet synchronous motor (IPMSM) drives may hold speed regulation even after electrical or mechanical faults have changed the stator-current signature. This paper aims to develop a ten-class diagnosis framework that separates healthy, single-fault and compound-fault operation under varying sp...

Reinforcement Learning for Molecule Design: A Taxonomy of Algorithms, Benchmarks, and Road to Synthesizable Leads

Cooperative multi-agent deep reinforcement learning for cost-aware metro–bus transfer coordination under fixed rail schedules

Automatic Operation Algorithm for Stormwater Pumping Stations Based on Heuristic-Guided Reinforcement Learning

Automated operation of urban stormwater pumping stations is a sequential decision-making problem that must prevent detention-basin overflow during intense rainfall while coordinating pump switching, pump use, and dry-running risk. This study proposes a heuristic-guided reinforcement-learning framework in which a geneti...

From Chronology to Physiology: A Critical Translational Assessment of Artificial Intelligence for Precision Healthspan Measurement

Healthspan, the period of life free from major chronic disease and disability, is a central geroscience outcome, yet its prediction remains difficult due to its multidimensional and heterogeneous nature. In this review, we synthesize advances in artificial intelligence (AI) for precision healthspan assessment, shifting...

Support-aware dual-agent reinforcement learning for soccer possession and action valuation with spatial–temporal modeling

Goal directed closed-loop training of network bursts in cortical cultures produces improved performance and error correction over days

Dissociated neural cultures are living systems with no evolutionary history in their current form, which makes them a testbed for asking what goals neural tissue can learn to pursue once it is removed from its evolved body. It remains unclear which features of network activity such a culture can be trained to control,...