reinforcement learning

MULTI-AGENT REINFORCEMENT LEARNING FOR ENERGY- CONSTRAINED FLEET COORDINATION AND DISRUPTION RECOVERY An Expanded Decision-Learning Framework for Electric Logistics Networks Shubham SS1, Shinde S2

Safe and optimal trajectory learning for autonomous racing via Deep Reinforcement Learning and Control Barrier Functions

Artificial Intelligence-Assisted Design for Enhanced Performance and Energy Efficiency in Computer Architecture

This research journal discusses how Artificial Intelligence (AI) and Machine Learning (ML) can enhance computer architecture design by addressing challenges in performance and energy efficiency. Traditional methods rely on expert intuition and exhaustive simulations, which are limited when exploring vast design spaces....

"CyberWarEnv2102026 Reproducible Registry — 160 Training Runs (EXP-2026-000001..000010)"

n=20، 160 runs SHA-256 verified، v2.1.0 ✅ License: CC-BY-4.0 ✅ Version: v2.1.0 ✅ Resource type: Dataset ✅ Keywords: reinforcement learning, cyber defense, reproducibility, PPO, DQN

Within Reach: Actions as Archetypes

An action is an archetype: substantiated structure for how a thing is done, laid down by what doing it has resolved or produced. This paper states that commitment formally within the Language of Stress, a value-primitive theory of consciousness, and follows what it does. It is the treatment that the constraint map for...

INTELLIGENT CAD/CAM INTEGRATION FOR ADAPTIVE MANUFACTURING: A SYSTEMATIC REVIEW OF AI AND IOT FRAMEWORKS IN INDUSTRY 4.0

The union of Artificial Intelligence and the Internet of Things (IoT) is transforming the way Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) systems are used in the Industry 4.0 production process. Based on the PRISMA 2020 reporting framework (Page et al., 2021), this report is a systematic literatu...

Agentic Software Issue Resolution with Large Language Models: A Survey

Software issue resolution task aims to address real-world issues in software repositories based on natural language descriptions provided by users, representing a key aspect of software maintenance. With the rapid development of large language models (LLMs) in reasoning and generative capabilities, LLM-based approaches...

Learning from Unreliable Trajectories: Adversarially-Robust Federated Q-Learning

We study federated reinforcement learning in which multiple agents interact with a common Markov decision process and communicate through a central server to collaboratively learn the optimal stateaction value function. Our goal is to understand whether the sample-efficiency benefits of collaboration can be retained wh...

Semantic Knowledge Management Through Cognitive Automation Using NLP and Ontological Reasoning for Contextual Semantic Inference and Intelligent Knowledge

NLP and cognitive reasoning are combined to fulfill the increasing requirements of intelligent and explainable knowledge management. The goal is to create an adaptive framework, which would be able to conduct contextual semantic inference and intelligent knowledge discovery. The difference is in the fact that transform...

Code and Data for Nonlocal Monte Carlo via Deep Reinforcement Learning

Code and Data for "Nonlocal Monte Carlo via Deep Reinforcement Learning"

Reproduction package for IJIES 20264970: Buffer-Aware Beam Hopping under Traffic Heterogeneity in Multi-Beam LEO Systems

Code and recorded results for IJIES paper 20264970. MATLAB R2025a with the Reinforcement Learning, Deep Learning, and Statistics and Machine Learning toolboxes. From the unzipped folder, run verify/run_verify_table5.m and verify/export_tables.m.

Collective reinforcement learning and human–machine cooperation in multi-state stochastic games

With the growing deployment of artificial intelligence agents in collaborative systems, understanding how they jointly shape cooperative behavior with humans under dynamic environmental conditions becomes essential. Stochastic games provide a natural framework for capturing how environmental feedback shapes behavioral...