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reinforcement learning

2,546 papers

#reinforcement learning Open access Sep 2026

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,...

Razia Bibi, Syeda Hoor Fatima, Atif Mahmood · 0 citations
#reinforcement learning Open access Sep 2026

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...

Fernando Borrás · 0 citations
#reinforcement learning Open access Sep 2026

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...

anonymous · 0 citations
#reinforcement learning Open access Sep 2026

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...

Sijin Yu, Yunbo Li, Jiaye Gong · 0 citations
#reinforcement learning Open access Sep 2026

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...

Fernando Borrás · 0 citations
#reinforcement learning Open access Sep 2026

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...

Unknown authors · 0 citations
#reinforcement learning Open access Sep 2026

Structured Episodic Memory (SEM)

Structured Episodic Memory (SEM) is an experimental non-neural agent architecture that combines overlapping state abstractions, associative value memories, eligibility-based temporal credit assignment, goal-conditioned contact control, and context-dependent strategy memory. We evaluate SEM in Pong using structured stat...

Berat Araç · 0 citations
#reinforcement learning Open access Sep 2026

Integrating Formula 3B (Learn, Practice, Compete) with the Application GO Expert as a Blended Learning Model: A Conceptualization in the Tutoring Context

The digital transformation of education has driven tutoring institutions to shift from conventional face-to-face instruction toward an integrated learning approach leveraging technology. One such approach is Formula 3B, a learning model developed by the author and implemented at Ganesha Operation (GO), the leading and...

Bob Foster · 0 citations
#reinforcement learning Open access Sep 2026

The Combination of RAG and RL: A Technological Breakthrough and Future Directions in Mathematical Reasoning with LLMs

Large language models (LLMs) face challenges in mathematical reasoning tasks, including logical disjunction, process hallucination, and a loss of creativity, which severely restricts the reliability and application of LLMs for this purpose. To tackle these issues, this paper applies secondary research, systematically e...

Yuchen Wang · 0 citations

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MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

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