reinforcement learning

A Comparative Study of Model Predictive Control and Deep Reinforcement Learning for Distributed Traffic Signal Control

This paper presents a comparative study of two distributed traffic signal control approaches: a cell-transmission-model-based model predictive control (MPC) method and a deep reinforcement learning (DRL) method based on latent spatio-temporal network (LST-Net). In the MPC method, each intersection predicts local traffi...

Digital Twins and Offline Reinforcement Learning for Mechanical Circulatory Support Decisions: What the Current Evidence Can and Cannot Support

Machine Learning-Based Prediction of the Dry Sliding Wear Behaviour of Al2O3-Al6061 Metal Matrix Composites

This study investigated the dry sliding wear behaviour of Al2O3 particulates (2–6 wt.%) in Al6061 metal matrix composites produced by ultrasonic stir casting, examining mechanical and tribological behaviour. We modelled wear rate using machine learning. Optical studies confirmed uniform Al2O3 particle dispersion, minim...

Advances in path planning for spherical tank inspection

Spherical tanks are widely used pressure special equipment in the petrochemical, energy, and metallurgical industries, and their regular non-destructive testing is a critical link to ensuring safe operation. Path planning technology directly determines the coverage completeness, operational efficiency, motion safety, a...

SADRL-paper: code and trade logs for "A Spread-Aware Deep Reinforcement Learning Framework Using Directional Changes Sampling for High Frequency FX Trading"

Code accompanying A Spread-Aware Deep Reinforcement Learning Framework Using Directional Changes Sampling for High Frequency FX Trading (G. Rayment, T. Papastylianou, M. Kampouridis), revised for Array. Attached: per-trade logs behind the paper's results. Every completed trade of every evaluation run, with executed ent...

ATTENTION ALLOCATION, SCARCITY & REINFORCEMENT AT THE LIMIT Selective Processing, Salience, Novelty, Attention Capture, Decay, Forgetting, and Future Accessibility

ATTENTION ALLOCATION, SCARCITY & REINFORCEMENT AT THE LIMIT Selective Processing, Salience, Novelty, Attention Capture, Decay,Forgetting, and Future Accessibility Feng Cheng-en (33) x Starli When does selective attention measurably reshape the future accessibility of meaning? ATTENTION ALLOCATION, SCARCITY & REINFORCEM...

AI-Tutor: Adaptive Feedback and Problem Recommendation for ACPC Training

A two-stage framework integrating group relative policy optimization with Tabu search for dynamic rescheduling in UAV testing

Modeling decision dynamics disentangles working memory, cognitive control and reinforcement learning and reveals clinical differences

Various neurocognitive processes contribute to reinforcement learning (RL) and decision making, requiring careful task designs and computational modeling to disentangle them. People rely on capacity-limited working memory (WM) to rapidly and flexibly adapt behavior during learning, together with slower incremental RL p...

Performance Analysis for Leveraging AI to Reduce Energy Consumption of a Data Center: A Safe Deep Reinforcement Learning and Workload-Aware Framework

Abstract Electricity demand from data centers is rising quickly with the deployment of artificial intelligence (AI), and cooling remains the largest non-IT load, particularly in hot-arid climates. This paper presents a performance analysis of leveraging AI to reduce the energy consumption of a 10 MW AI/cloud data cente...

Rectifying market externalities via AI policymaking

Abstract Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as the cost of over-appropriating a common-pool resource (which diminishes future stock, and thus harvest, for everyone). Quantifying appropriate interventions to market price...

From Optimal Policies to Individual Differences: Rethinking Reinforcement Learning for Biology