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2,609 papers
This paper investigates the challenges of achieving robust cooperative navigation in multi-agent reinforcement learning (MARL) scenarios, addressing the limitations of relying solely on extrinsic rewards. We propose a novel framework integrating intrinsic motivation – specifically, curiosity and surprise – into each ag...
This paper investigates the use of dynamic constraint satisfaction within fractal generation algorithms to enhance structure complexity and robustness. Traditional fractal algorithms often rely on fixed constraints, limiting the potential for intricate and diverse forms. We propose an algorithmic approach that utilizes...
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...
This paper presents a resilient learning‐based adaptive sliding‐mode consensus control framework for nonlinear multi‐agent systems subject to switching communication topologies and actuator‐side cyberattacks. A distributed sliding‐mode leader‐follower tracking consensus protocol is first developed to ensure robust co...
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Quantum field theory, a cornerstone of modern physics, traditionally employs static, predefined geometries to represent and model quantum phenomena. However, limitations in this approach hinder the exploration of novel quantum states and emergent properties. This paper proposes a novel dynamic fractal geometry algorith...
This paper explores the fusion of nonlinear time series systems (NTS) modeling with reinforcement learning (RL) for addressing complex control problems characterized by high nonlinearity and time-varying dynamics. The core idea is to leverage the representation power of NTS models to capture system behavior and then ut...
The Single-Machine Scheduling Problem with Sequence-Dependent Setup Times (SMSP-SDST) is a classical NP-hard combinatorial optimization problem encountered in intelligent manufacturing, production planning, semiconductor processing, logistics, and adaptive production systems. Conventional exact optimization techniques...
This paper explores the design and implementation of a novel self-organizing constraint network architecture, tentatively named "Self-Organizing Constraint Networks with Adaptive Noise," leveraging reinforcement learning and quantum-enhanced constraint adaptation. The core claim centers around dynamically adjusting pri...
Abstract Deep learning models have achieved near-perfect accuracy in plant disease classification within controlled laboratory settings; however, their deployment in real-world agricultural environments is severely hindered by the “deployment gap”—a critical vulnerability to environmental corruptions such as sensor noi...
This paper investigates the application of reinforcement learning (RL) for adaptive quantum control. Traditional quantum control methods often rely on pre-designed pulse shapes optimized for specific scenarios, lacking adaptability to variations in system parameters or environmental noise. This work proposes a novel fr...
This paper introduces a novel matrix operation algorithm based on adaptive learning. Traditional matrix operations often rely on fixed parameters, limiting their efficiency when dealing with dynamic input matrices. The proposed algorithm dynamically adjusts the operation parameters based on the input matrix's character...
This document contains a comprehensive evaluation of the preprint "General Error Accumulation Relation" by Stamelos Loutsos (September 2026). The original work addresses a fundamental problem in computational neuroscience and reinforcement learning: how the microscopic Bellman/Temporal‑Difference update rules relate to...
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.
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.
Education Innovation
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
A Blog post by NVIDIA on Hugging Face
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