This dataset accompanies the manuscript "Planning for Leakage-Aware Collaborative Carbon Mitigation: Optimizer-Informed Multi-Agent Reinforcement Learning in Shandong's Dual Metropolitan Areas" submitted to the Journal of Urban Planning and Development (ASCE). It contains the full numerical inputs, calibrated artefacts...
Naibin Jiang, Zhen Peng· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to reinforcement learning (RL) that combines the strengths of hierarchical reinforcement learning (HRL) and memory networks. Traditional RL methods often struggle with complex tasks due to the curse of dimensionality and the difficulty of learning long-term dependencies. Hierarchica...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel reinforcement learning framework for robot control, leveraging adaptive reinforcement learning (ARL) to dynamically adjust control strategies based on both environmental feedback and the robot's internal state. Traditional reinforcement learning models often suffer from static policies, li...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This dataset supports the systematic review article "Sustainable Agro-Food Systems in the Digital Era: A Systematic Review of Economic, Technological, and Managerial Perspectives", which synthesises evidence from sixteen (16) studies examining how economic instruments, technological innovations, and managerial governan...
Muzamil Muzamil, Obel Obel, A. (Ade Tika) Herawati et al.· Zenodo (CERN European Organi...· 0 citations
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Dynamic Geometry for Adaptive Machine Learning addresses the limitations of static machine learning models by introducing a mechanism for dynamically adjusting geometric properties during training. This novel approach leverages quantum annealing to optimize for robustness and efficiency, ultimately enhancing generaliza...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Abstract Digital therapeutics (DTx) represent an emerging category of evidence-based healthcare interventions that use software to prevent, manage, or treat diseases and disorders. Unlike conventional wellness applications, DTx are designed around therapeutic objectives and are expected to demonstrate clinical benefits...
Zahid Hussain· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to parameter optimization utilizing adaptive neural networks, specifically designed to model complex function structures. The core innovation lies in integrating function structure modeling directly into the optimization process, enabling automated learning and optimization of the c...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This study provides technical support for trajectory planning and coordinated vehicle control of intelligent vehicles, contributing to high-precision obstacle avoidance and multi-objective coordinated control of connected and automated vehicles.
Yi Luo, A.-J. Niu· Frontiers of Mechanical Engi...· 0 citations
Relational Reinforcement Learning (RRL) aims to address the challenges posed by environments where actions influence the state, creating complex dependencies between states. However, traditional RRL methods often struggle to scale effectively, particularly in environments with intricate relational structures. This pape...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to designing distributed control systems utilizing Multi-Agent Reinforcement Learning (MARL). Traditional distributed control systems often rely on manually engineered control strategies, leading to limitations in adaptability, robustness, and scalability. This work introduces a fra...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
MATLAB source code supporting the research article “QMDP-PER-DDQN Trajectory Tracking for Mecanum Robots under Partial Observability.” This repository provides the MATLAB implementation of a belief-aware trajectory-tracking framework for a four-Mecanum-wheel mobile robot operating under noisy and partially observable s...
Oorappan G M, Lakshmanan S A, S Vignesh· Zenodo (CERN European Organi...· 0 citations
This paper introduces Adaptive Neuron Growth Networks (ANGNs), a novel neural network architecture designed for dynamic adaptation to varying input complexities and network performance. ANGNs utilize a reinforcement learning-based mechanism where individual neurons dynamically adjust their parameters – both synaptic we...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
MIT News · Artificial Intelligence· news.mit.eduOct 27, 2026
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
