reinforcement learning

Deep reinforcement learning-based collaborative task offloading and secure transmission optimization for UAV swarms

Deep reinforcement learning for optimizing mobile depots allocation and hybrid order dispatch in last-mile delivery under multi-participants behavioral uncertainty

Metric-aware multi-objective deep reinforcement learning for database knob tuning

Stabilizing independent multi-agent reinforcement learning via curriculum-based iterative self-play

Data-driven distributed reinforcement learning optimal cooperative output regulation control for T-S fuzzy MASs under DoS attacks

Physics-informed residual reinforcement learning via expert prior knowledge for safe and efficient autonomous merging

Verification Is Not Validity: From Self-Verifiable Rewards to Governed Execution

A control-plane analysis distinguishing three concepts routinely collapsed in contemporary AI discourse: event verifiability, proxy validity, and governance sufficiency. Taking Reinforcement Learning with Self-Verifiable Rewards and its SpyRL instantiation as its case, it accepts the engineering contribution — an envir...

What Is Artificial Intelligence?

This chapter explores the transformative role of artificial intelligence (AI) and machine learning (ML) in modern healthcare and medical training. We first outline the rapid progress of AI, fueled by the digitization of health data and advances in computing, which has led to significant shifts in how clinical data and...

Off-Policy Inverse Q-Learning Algorithm with Unobservable States

This paper investigates the output-feedback inverse reinforcement learning (IRL) problem and proposes a data-driven output-feedback off-policy inverse Q-learning algorithm that relies solely on input–output trajectory data from an expert system to address the problem of an unknown performance index in discrete-time lin...

Theoretical Aspects of Reinforcement Learning: A Special Issue of AMOP

This work states that there has been an upsurge of interest and development of RL theory and applications in the communities of stochastic control, stochastic analysis and unsupervised learning.

Smart HVAC Control Strategies for Optimizing Thermal Comfort and Energy Efficiency in Omani Residential Buildings Under Extreme Heat Conditions

Heating, Ventilation, and Air Conditioning (HVAC) systems account for 60–70% of residential electricity consumption in Oman, where extreme desert climate, with temperatures regularly exceeding 45 °C create substantial cooling demands. Unlike general reviews of smart HVAC controls, this study specifically evaluates the...

Literature Synthesis Matrix

A comprehensive extraction grid covering the 21 selected primary studies. Synthesizes structural data including author, publication venue, process mining domain, reinforcement learning approach, and explainability (XAI) focus.