reinforcement learning

The ‘stickiness’ of disgust: Testing the hypothesis of disgust intractability in the context of instrumental reversal learning.

Discrete emotion theories conceptualise fear and disgust as distinct emotions. This notion is supported by the finding that Pavlovian disgust conditioning is more resistant to extinction than fear conditioning—disgust may be ‘stickier’ than fear. Dimensional emotion theories challenge this, suggesting that categorical...

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...

Digital Twin-Based Simulation of Electric VehicleInteraction within Smart City Energy Networks

This work provides a comprehensive roadmap for developing intelligent, sustainable and resilient EV charging ecosystems that will resolve technical, economical, and ecologicalconsiderations while protecting the privacy of data and securing the system.

Residual-Gated Diffusion Augmentation for Multi-Agent Reinforcement Learning in Energy Management

Energy-Adaptive Online Continual Learning Framework with Dynamic Model Compression for Real-Time Sensor-Driven Embedded Systems

3D autonomous dynamic path planning for UUVs based on obstacle motion prediction and reinforcement learning

Unmanned Underwater Vehicles (UUVs) are widely applied in marine exploration and other missions due to their low cost and high reliability. Point-to-point path planning serves as the foundation for accomplishing various marine tasks. The limitations of sonar sensor detection pose significant challenges for UUV obstacle...

Reinforcement Learning for the 6th Generation in Advanced Communications

Hybrid modeling and energy optimization of NGL demethanizers using deep reinforcement learning

Abstract Cryogenic NGL demethanizers suffer from energy inefficiencies due to non-convex thermodynamic constraints. This study aims to implement a Deep Reinforcement Learning (DRL) framework to optimize energy consumption while respecting complex operational boundaries. A Soft Actor-Critic (SAC) agent was employed, tra...

Learning-Assisted Energy Optimization of Electric Vehicle Fleets in Renewable-Rich Microgrids

In this paper, we propose a learning-enabled framework to support the energy management of EV fleets operating in renewable-rich microgrids. The framework leverages a combination of Reinforcement Learning, Model Predictive Control, and Probabilistic Renewable Forecasting to optimize the charging schedules of EVs and th...

AI Driven Jamming in 6G Networks

This paper investigates AI-driven jamming as an emerging security threat to sixth-generation (6G) wireless networks. Unlike conventional jamming techniques that rely on relatively fixed and predictable interference patterns, AI-driven jamming can use machine learning to sense network conditions, adapt attack strategies...

Marwa-Chouhdary/MEC_IIoT_E_DDPG_LS_MCOR_TD3_Code: MEC IIoT E-DDPG-LS and MCOR-TD3 Code v1.0.0

MCOR-TD3 for MEC-Enabled IIoT — v1.0.1 This release contains Python code associated with the paper "Adaptive Task Caching and Resource Allocation in MEC-Enabled IIoT Using Deep Reinforcement Learning." It supports simulation of cooperative task offloading and computing-resource allocation using MCOR-TD3 to minimize max...

Causal-Graph-Guided Reinforcement Learning for LLM Security Testing: An Empirical Study

This preprint investigates whether structural causal information derived from adversarial interaction trajectories can improve reinforcement-learning-based black-box security testing of large language models. The study combines PPO-based adaptive prompt search with an interpretable six-factor representation derived fro...