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reinforcement learning

2,609 papers

#reinforcement learning Open access Sep 2026

Enhancing Conceptual Coherence: Students’ Mental Models of the Male Reproductive System through Collaborative Constructive Approach

This study aims to analyze the effect of the Collaborative Constructive approach on the quality of students’ mental models in the topic of the male reproductive system. The study employed a mixed-methods approach with an embedded experimental design and a quasi-experimental pretest-posttest control group design. The re...

Anindya Utami Putri, Adi Rahmat, Yanti Hamdiyati · 0 citations
#reinforcement learning Open access Sep 2026

Dynamic Topology Algorithm Reinforcement Learning (DTARL)

This paper introduces the Dynamic Topology Algorithm Reinforcement Learning (DTARL), a novel reinforcement learning framework designed to optimize complex systems by dynamically adjusting topology. Traditional topology optimization methods often rely on manual parameter tuning, while DTARL leverages reinforcement learn...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

Dynamic Graph Partitioning for Large-Scale Data Analysis

Large-scale graph analysis, prevalent in domains like social network analysis, bioinformatics, and knowledge graph management, faces significant challenges due to the memory limitations of traditional graph processing techniques. This paper introduces a dynamic graph partitioning algorithm designed to mitigate these is...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

Title: Adaptive Quantum Algorithm for Non-Linear Optimization

This paper introduces an innovative adaptive quantum algorithm designed to address the limitations of traditional optimization techniques, particularly when dealing with non-linear problems. The algorithm leverages reinforcement learning to dynamically adjust its parameters based on the input data, offering a significa...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

Adaptive Tensor Decomposition for High-Dimensional Sparse Data

High-dimensional sparse data poses significant challenges for traditional tensor decomposition techniques. These methods often suffer from instability, poor generalization, and difficulty in capturing the underlying structure of the data due to the curse of dimensionality and the irregular sparsity patterns. This work...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

Algorithmic Self-Organizing Constraint Networks

This paper explores the design and implementation of an Algorithmic Self-Organizing Constraint Network (ASOCN) architecture, a novel approach to constraint management within complex systems. The core claim centers on the development of a dynamic constraint adjustment system leveraging iterative reinforcement learning t...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

Dynamic Graph Partitioning for Large-Scale Data Analysis

Large-scale graph analysis, prevalent in domains like social network analysis, bioinformatics, and knowledge graph management, faces significant challenges due to the memory limitations of traditional graph processing techniques. This paper introduces a dynamic graph partitioning algorithm designed to mitigate these is...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

Title: Algorithmic Chaos Theory for Optimal Pattern Generation in Complex Networks

This paper explores the application of algorithmic chaos theory to generate optimal and aesthetically pleasing patterns within complex networks. Traditional approaches often struggle with creating truly novel and captivating designs, while chaos theory offers a potential pathway to unpredictable beauty. We propose a no...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

Reconciling top-down and bottom-up conservation priorities with reinforcement learning from human feedback

Abstract To meet the Kunming-Montreal Global Biodiversity Framework, spatial planning must upscale area-based conservation, while ensuring equitable governance and local community integration. Historically, a deep divide has persisted between data-driven, top-down systematic conservation planning, often performed at la...

Daniele Silvestro, Stefano Goria, E‐Ping Rau et al. · 0 citations
#reinforcement learning Open access Sep 2026

Non-Linear Matrix Dynamics Theory

This paper introduces a novel dynamic mapping theory for non-linear matrices, aiming to facilitate machine learning algorithms through automated matrix adaptation and optimization. We propose a framework where the matrix's dynamic evolution is modeled as a physical process, allowing for learning and control of this evo...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

Adaptive Learning-Based Distributed System Resource Scheduling

This paper presents a novel approach to distributed system resource scheduling based on adaptive learning. Traditional resource scheduling methods often rely on static rules and predefined policies, which struggle to adapt to the dynamic and unpredictable nature of distributed environments. Our proposed system leverage...

Jincheng Zhang · 0 citations
#reinforcement learning Open access Sep 2026

Optimizing the spatial layout of urban service facilities under benefit uncertainty: a Bayesian-guided hierarchical reinforcement learning framework

Spatial layout optimization of urban service facilities aims to maximize system-wide locational benefits through rational facility placement. However, due to pronounced spatial heterogeneity and nonlinear interactions among urban environmental features, locational benefits vary significantly across regions and are subj...

Yijie Lyu, Baoju Liu, Yalun Li et al. · 0 citations

From tech blogs

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Microsoft Research Blog Nov 3, 2026

Learn the rules: The hacker mindset

Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.

Google DeepMind Blog Nov 3, 2026

Agentic cheminformatics with Google Antigravity

Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.

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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