reinforcement learning

Reward expectation signals in the superior colliculus during associative learning

Predicting future rewards allows neutral sensory cues to acquire motivational significance and guide goal-directed actions that maximise reward acquisition1. These reward predictions are updated when outcomes differ from expectations resulting in an error in prediction2. Midbrain dopamine neurons provide a canonical ne...

Multi-agent joint optimization reinforcement learning algorithm based on impedance matching space segmentation

Reinforcement learning with control-barrier-function safety filters for trajectory tracking on a Stewart platform: campaign v3

Code, aggregated data and provenance for the simulation campaign behind the manuscript. Twelve training seeds, twenty-five evaluation episodes per seed on a fixed shared evaluation base, four arms. Raw policy checkpoints and TensorBoard logs are not included: they exceed two gigabytes and every reported number is deriv...

Positive Teaching and School Wellbeing among Students

The present study explores the role of positive teaching practices in promoting school wellbeing among students using a qualitative research approach. Guided by principles of positive psychology, the study aimed to understand students’ lived experiences of supportive teaching behaviors and their influence on emotional,...

Bayesian Deep Reinforcement Learning for Autonomous Driving with Uncertain Pedestrian Intent

Research on Safe Decision-Making for Highway Scenario Autonomous Driving Based on Deep Reinforcement Learning

Improved Deep Reinforcement Learning Approach for Lane-Level Variable Speed Limit Control of Mixed Traffic Flow

Fábrica de Modelos — herramienta IA para la academia (UMH)

Recorrido interactivo por las siete etapas de construcción de un modelo de lenguaje —tokenización BPE, atención, preentrenamiento, ajuste a instrucciones, refuerzo e inferencia— con un Transformer que se entrena de verdad en JavaScript dentro de la propia página, sin instalar nada. Material didáctico construido sobre e...

MulTADrug-GPT: A sample-efficient generative framework for multi-target drug design via reinforcement and preference learning

This repository contains the code, data, and pre-trained models for "MulTADrug-GPT: A sample-efficient generative framework for multi-target drug design via reinforcement and preference learning". For detailed instructions on environment setup and the step-by-step execution of the training and evaluation pipelines, ple...

A risk-aware multimodal temporal reinforcement learning approach for collision avoidance of unmanned surface vehicles in multi-dynamic-obstacle scenarios

This study addresses autonomous collision avoidance for unmanned surface vehicles in multi-dynamic-obstacle maritime scenarios, where the number of obstacles varies, risk interactions are complex, and dynamic environmental evolution is difficult to exploit effectively. A Risk-Aware Multimodal Temporal Proximal Policy O...

Fábrica de Modelos — herramienta IA para la academia (UMH)

Recorrido interactivo por las siete etapas de construcción de un modelo de lenguaje —tokenización BPE, atención, preentrenamiento, ajuste a instrucciones, refuerzo e inferencia— con un Transformer que se entrena de verdad en JavaScript dentro de la propia página, sin instalar nada. Material didáctico construido sobre e...

AGENTIC SOCIO-TECHNICAL SYSTEMS FOR AGING DEMOGRAPHICS: INTEGRATING GRAPH NEURAL NETWORKS AND DEEP REINFORCEMENT LEARNING FOR RESILIENT HEALTHCARE LOGISTICS

The fast-growing aging demographics in both developed and developing countries are exerting increasing stress on healthcare systems, especially those that experience high old-age dependency rates.Healthcare logistics models based on traditional methods tend to employ static and reactive decision-making strategies, whic...