reinforcement learning

AI-Based Wireless Communication Systems Enabling Innovative Smart Systems Design

This chapter explores how Artificial Intelligence (AI) will support the future of wireless communication systems through advances in 6G networks, IoT ecosystems, and edge computing technologies. Technologies that enhance machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms to improve ne...

PIREUS: Autonomous Operator Foundry, Hardware Capability Ontology, and Verifiable Reinforcement Learning Compiler Framework for Sounio

PIREUS is an autonomous operator foundry, hardware-capability ontology engine, and verifiable reinforcement learning compiler framework designed for the Sounio programming language ecosystem. It operates at the intersection of non-associative Cayley-Dickson hypercomplex algebras, formal silicon microarchitecture ontolo...

Deep reinforcement learning-optimized adaptive authenticated encryption and dynamic transmission control for secure LoRaWAN IoT communication

The growth of Internet of Things (IoT) devices in the industrial, environmental and urban sectors has raised the need for secure, energy efficient, and adaptive communication solutions. The LoRa (Long Range) technology combined with the LoRaWAN protocol stack is very promising due to its low power consumption, ability...

Rehabilitation Engineering Explained: Assistive Technology & Human Recovery Systems

This open educational resource (OER) curriculum module delivers a comprehensive, mathematically grounded foundation in Rehabilitation Engineering, detailing technologies that restore, augment, and support physical mobility and cognitive independence for individuals with neuromuscular and musculoskeletal impairments. Th...

Hierarchical Multi-Agent Reinforcement Learning for Cooperative Wildfire Suppression with Amphibious Firefighting UAVs

Results indicate that the hierarchical architecture provides the most pronounced contribution under the tested settings, while LSTM, the level-specific centralized critics, and hierarchical rewards provide additional performance contributions under the tested settings.

Geometric Shielding and Lagrangian Constraints in Safe Reinforcement Learning: A Factorial Study

Safe reinforcement learning requires balancing task performance against safety constraints, yet how different safety mechanisms interact when combined remains an open question. We present a systematic empirical comparison of four reinforcement learning algorithms, PPO, SAC, RCPO (with fixed λ), and Lagrangian PPO (LagP...

PIREUS: Autonomous Operator Foundry, Hardware Capability Ontology, and Verifiable Reinforcement Learning Compiler Framework for Sounio

PIREUS is an autonomous operator foundry, hardware-capability ontology engine, and verifiable reinforcement learning compiler framework designed for the Sounio programming language ecosystem. It operates at the intersection of non-associative Cayley-Dickson hypercomplex algebras, formal silicon microarchitecture ontolo...

Supplementary Document for Origin-Destination-Aware ConstrainedGraph Reinforcement Learning for Dynamic Electric Vehicle Routingand Charging Coordination

Persistence-Aware Reward Design for Closed-Loop Reinforcement Learning-Based Network Intrusion Defense

Quantifying Pilot Operational Deviations Based on Flight Data via Inverse Reinforcement Learning and Conformal Prediction

Threshold-based flight data monitoring (FDM) identifies parameter exceedances but provides limited visibility into gradual operational deviation within nominal operating bounds. This study presents a three-layer framework for continuous operational deviation quantification from quick access recorder (QAR) data. A stand...

Coordinated Optimization Strategy for Load Aggregator in Distribution Network Considering Demand Response and Peak Regulation Incentive

With the increasing proportion of flexible resources such as distributed generation, energy storage and demand response in new power systems, load aggregators, as an important subject connecting users and the market, face the challenges of complex load types, large differences in response capabilities, and high peaking...

Urban Vehicle Route Choice Modeling Based on Multi-Source Trajectory Data and Adversarial Inverse Reinforcement Learning

To address the limitations of traditional route choice models, including their dependence on manually constructed path sets, their difficulty in capturing nonlinear decision preferences, and their insufficient consideration of dynamic congestion factors, this paper proposes an urban vehicle route choice modeling method...