reinforcement learning

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...

Project TALOS: Tactical Agentic Literature Orchestration System

Project TALOS is an autonomous research intelligence platform powered by deep reinforcement learning (DDDQN), multi-tier LLM orchestration, and the Grey Wolf Optimizer (GWO). It conducts end-to-end scientific literature discovery and evaluation across 18 academic APIs.

Cognitive therapy for brain diseases using deep learning models

Particle Swarm Optimization for Variable Load Rate and Coal Consumption Enhancement in Thermal Power Units

The global energy transition requires coal-fired power plants to transition from base-load sources to flexible regulation resources, necessitating a critical balance between rapid load adjustment capabilities and operational economic efficiency. To address the nonlinear, strongly coupled, and multi-constraint character...

Data-Driven Pricing Strategies

In store environments with a lot of competition, dynamic and competitive pricing has become an important way to make the most revenue while gaining the largest percentage of the market. Cost-plus and steady margin pricing models are becoming less useful because they are unable to take into account how competitors are r...

pv-residual-rl: network-aware rooftop PV subsidy planning with an exact allocation layer and residual reinforcement learning

Simulator, baselines, experiment scripts, per-scenario results and trained models for the multi-year allocation of a rooftop-PV subsidy budget across the 32 neighbourhoods of an IEEE 33-bus feeder under uncertain acceptance, demand growth and irradiance (19 years of PVGIS data for Tehran). The package contains the LinD...

pv-residual-rl: network-aware rooftop PV subsidy planning with an exact allocation layer and residual reinforcement learning

Simulator, baselines, experiment scripts, per-scenario results and trained models for the multi-year allocation of a rooftop-PV subsidy budget across the 32 neighbourhoods of an IEEE 33-bus feeder under uncertain acceptance, demand growth and irradiance (19 years of PVGIS data for Tehran). The package contains the LinD...

Event-Triggered Pinning Control for Cooperative Ramp Merging in Mixed Traffic via Graph Reinforcement Learning

This paper proposes event-triggered pinning graph reinforcement learning (EPGRL) for cooperative ramp merging in mixed traffic with connected and automated vehicles (CAVs) and human-driven vehicles. EPGRL represents vehicle interactions as a time-varying directed graph and uses a dual-stream graph encoder with temporal...

Coordinated Offloading Cooperative Multi-Agent Reinforcement Learning for the Edge–Cloud Continuum

The increasing demand for intelligent, low-latency services in edge–cloud continuum systems poses new challenges for dynamic and efficient task offloading. We propose a Multi Agent Reinforcement Learning (MARL) framework for dis tributed task offloading under partial observability, where each device offloads only a por...

Affect-Aware Game Personalization with Reinforcement Learning: How to Improve Players' Engagement, Performance, and Emotions

Adaptive games are often evaluated through performance alone, even though gameplay experience also depends on players’ affective responses. We present an affect-aware game adaptation approach that combines a control loop architecture with tabular model-free reinforcement learning (MFRL) to adjust discrete game and user...