reinforcement learning

Multi-Agent Reinforcement Learning for Dynamic Parallel-Machine Scheduling with Human–Robot Collaboration and Worker Fatigue

This study investigates a dynamic parallel-machine scheduling problem with human–robot collaboration (DPMS-HRC), where dynamically arriving jobs are assigned to eligible workstations and processed by workers, robots, or human–robot collaboration based on real-time system states. Worker-involving modes improve operation...

Topological Quantum Control of Attosecond Molecular Dynamics: A Proof-of-Principle Framework on Reduced-Dimensional Models

The El-Rakhawi Framework for Topological Quantum Control (DOI: 10.5281/zenodo.22907946) presents a proof-of-principle methodology integrating Uhlmann-based robustness metrics with Reinforcement Learning (PPO) to control open quantum systems governed by the Lindblad master equation. By embedding an Uhlmann Robustness Me...

Closed-Loop, Event-Driven Machine Learning for Autonomous Triage of Single-Photon Emitters

Abstract Identifying which candidate sites on a solid-state sample are single-photon emitters requires a Hanbury Brown–Twiss (HBT) measurement of the second-order correlation function g(2)(τ), which is slow because photon coincidences accumulate at a rate proportional to the square of the detected count rate. We examin...

THE EL-RAKHAWI FRAMEWORK FOR MICROBIAL ECOLOGICAL COMPUTING: ENGINEERING BIOCHEMICAL SIGNALING PROTOCOLS FOR COLLECTIVE INTELLIGENCE AND SELF-ADAPTATION IN DISTRIBUTED BIOLOGICAL

The El-Rakhawi Framework for Microbial Ecological Computing (MEC) reconceptualizes microbial communities as distributed computational systems, treating individual cells as processing nodes, autoinducers as data packets, and quorum sensing as biochemical communication protocols analogous to the OSI networking stack. Add...

Empowerment Gain as Causal Learning, Causal Learning as Empowerment Gain: A Bridge between Bayesian Causal Hypothesis Testing and Reinforcement Learning

Abstract Cognitive scientists have applied advances in our formal understanding of causation in philosophy and computer science, particularly within the Causal Bayes Net formalism, to understand human causal learning. In parallel, reinforcement learning (RL) researchers have developed the idea of an intrinsic reward si...

Adaptive Switching Between Self-Reinforcement and Mutual Reinforcement in Multi-Agent Systems: A Dynamic Framework for Individual and Collective Intelligence Optimization

Multi-agent systems can improve their performance through individual learning and inter-agent cooperation. However, relying exclusively on individual reinforcement may limit the exploitation of complementary knowledge, whereas persistent mutual reinforcement may introduce communication overhead, correlated errors, exce...

Decoupled multi-dimensional reinforcement learning with temporal communication for vision-based UAV control in partially observable environments

Modeling Patient Care Pathways as Markov Chains

Healthcare involves complex processes where patients move through multiple stages of care. Understanding these pathways is essential for improving outcomes. This chapter presents a pedagogical, worked-example application of Markov chain modeling to patient care pathways using the MIMIC-III Clinical Database Demo. The m...

Adaptive State of Charge and State of Health Estimation through Optimized Deep Reinforcement Learning with Dynamic Agent Selection and Test-Time Training Tailored to Dynamic Operating Conditions

Abstract Accurately estimating the state of charge (SOC) and state of health (SOH) of lithium−ion batteries under dynamic conditions is critical for electric vehicles (EVs) and energy storage systems; however, nonlinearity and uncertainties hinder traditional methods. This paper proposes an adaptive estimation framewor...

Adaptive Switching Between Self-Selection and Collaborative Selection in Multi-Agent Systems: A Unified Decision-Making Framework

Multi-agent systems must repeatedly select actions, solutions, resources, partners, strategies, or organizational configurations while operating under uncertainty and limited computational resources. Existing approaches often emphasize individual decision-making, collaborative decision-making, or predefined coordinatio...

Text to SPARQL via LLMs fine-tuned with rewards and reasoning

Large language models have made notable progress in translating natural-language questions into SPARQL queries for knowledge graphs. Recent studies from the first Text2SPARQL challenge have explored zero-shot and few-shot scenarios, as well as in-context learning techniques. With state-of-the-art (SoTA) results, the ch...

A Hybrid Graph Neural Network–Deep Reinforcement Learning Framework for Robust Multi-Modal Sensor Fusion and Real-Time Dynamic Obstacle Detection in Autonomous Environments

The research presents an intelligent framework for autonomous systems that performs multimodal sensor fusion to improve the identification of obstacles operating in dynamic scenarios.The proposed solution allows robots to handle complex and messy situations.The system leverages graph neural networks to capture spatial...