AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,335 papers
Geometric Information Learning is a rapidly growing field with the potential to significantly enhance image recognition and object detection tasks. This paper explores the application of reinforcement learning to learn geometric information learning algorithms, aiming to improve the accuracy of these tasks. We propose...
This paper presents a novel approach to robot swarm coordination control utilizing Multi-Agent Reinforcement Learning (MARL). The core claim is to design a robust and adaptive robot swarm control system by leveraging the learning capabilities inherent in MARL. The proposed system employs a federated reinforcement learn...
This paper investigates the application of adaptive reinforcement learning (RL) for efficient hardware resource management. Traditional hardware resource management often relies on static configurations and manual tuning, which can be inefficient and unresponsive to dynamic workloads. This research proposes a novel app...
This paper investigates the application of reinforcement learning (RL) to the problem of adaptive model selection within deep learning. Traditional model selection methods often rely on manual tuning or grid searches, which can be computationally expensive and inefficient. We propose a framework where a reinforcement a...
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This paper addresses the critical challenge of concept drift in continuous learning systems. Traditional machine learning approaches often assume a static environment, leading to performance degradation when the underlying data distribution changes over time. This work proposes a novel framework for continuous learning...
This paper introduces a novel adaptive differential privacy (DP) mechanism designed to mitigate the limitations of traditional DP approaches, particularly concerning excessive noise addition in scenarios with small datasets. The core idea is to dynamically adjust the noise scale based on the sensitivity of the query an...
Deep Reinforcement Learning (DRL) has achieved remarkable success in various domains, including game playing and robotics. However, the inherent uncertainty and complexity of DRL policies pose significant challenges to safety and reliability. This work introduces a novel approach to formally verify the safety and robus...
This paper presents a novel decentralized multi-agent reinforcement learning (MARL) algorithm designed to address the challenges posed by environmental uncertainty and information asymmetry in multi-agent systems. The core of the algorithm lies in leveraging communication between agents, inspired by approaches like MAD...
This paper proposes a novel approach, Neural Network Adaptive Topology Optimization (NNAUTO), for designing and optimizing neural network architectures. The core idea is to dynamically adjust the topology of a neural network during training using a reinforcement learning (RL) agent, guided by a physics-informed energy-...
(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Reinforcement learning with verifiable rewards is the standard route to reasoning-tuned language models, and the field has split over whether its gains leave the training domain. One body of work reports that most models succeeding at mathematics fail to transfer a...
This record contains the supplemental materials associated with the manuscript entitled “Constrained Reinforcement Learning Scheduling for Low-Carbon IES Considering P2G-CCS Coupling and District Heating Network Thermal Inertia.” The supplemental materials include standard SAC update equations, the complete Action Proj...
This paper presents a novel approach to digital signal processing (DSP) utilizing reinforcement learning (RL). Traditional DSP algorithms are frequently designed with static parameters, rendering them ineffective when faced with dynamic or non-stationary signals. The proposed method leverages RL to train a DSP algorith...
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Learn more about our approach to safety and security for Gemini 4 Argon.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
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