reinforcement learning

Deep Adaptive Reinforcement Learning‐Based Energy Efficient Clustering and Resource Allocation Mechanism in Ultra‐Dense Network

ABSTRACT Due to the quick enhancement of the 5th Generation (5G) wireless communications, the user count is enhancing relatively. So, Ultra‐Dense Networks (UDNs) are becoming very significant for assisting various users and enhancing mission‐critical applications. To link numerous user systems and offer better data rat...

Bipedal Navigation and Locomotion Control Algorithms for Humanoid Robots

With their anthropomorphic design, humanoid robots are naturally adapted to human environments; however, high degrees of freedom, severe nonlinear dynamics, and small stability margins make autonomous locomotion a major bottleneck for practical applications. This paper surveys the key algorithms for humanoid robot loco...

ROSE: Reinforcement Learning Optimised Spectrum Exploration for Intelligent Electronic Warfare Scanning

This paper presents ROSE (Reinforcement Learning Optimised Spectrum Exploration), an intelligent spectrum scanning framework designed for wideband Electronic Warfare (EW) environments. The proposed system addresses the challenge of monitoring a wide radio-frequency spectrum when a receiver has limited instantaneous ban...

HGWO-RL: hybrid grey wolf optimization with Q-learning refinement for energy-aware multi-objective task scheduling in heterogeneous fog–cloud networks

Energy-aware task scheduling in fog–cloud IoT networks must balance monetary cost, deadlines and energy. We propose hybrid grey wolf optimizer-reinforcement learning (HGWO-RL), which combines a grey wolf optimizer (GWO) with a tabular Q-learning agent that selects move operators to refine the three elite solutions. The...

Reinforcement Failing guides the discovery of emergent physical dynamics in adaptive tumor therapy

Abstract Artificial intelligence is revolutionizing scientific discovery in medicine, with reinforcement learning (RL) emerging as a promising tool for optimizing therapeutic strategies. Yet applying RL to complex scenarios such as therapy dynamics in solid tumors is constrained by the challenge of constructing trainin...

Multi-agent deep reinforcement learning for resilient recovery of electric–gas coupled energy systems under extreme disasters

Data‐Driven Harmonic Measurement, Reliability Enhancement, and Adaptive Power Flow Optimization for Renewable Energy Integrated Smart Microgrids

ABSTRACT The increasing penetration of renewable energy sources in smart microgrids introduces significant challenges related to power quality degradation, harmonic distortion, operational uncertainty, and system reliability. Conventional centralized control approaches often suffer from communication bottlenecks, limit...

Digital twin network-based communication chip optimization and signal processing for 6G networks

This paper proposes a Digital Twin Network-based Communication Chip Optimization Framework (DTN-CCOF) for intelligent signal processing in 6G networks. The framework constructs a real-time virtual model of physical communication chips by integrating chip parameters, channel characteristics, and network conditions. A mu...

Minimal Agents Can Generate and Self-regulate Novel Social Behaviors Using Just Reinforcement Learning

Machine Learning-Enabled Plasma Applications

Machine learning (ML) is transforming plasma engineering by enabling efficient analysis and control of complex, nonlinear plasma systems. Plasma processes, widely used in semiconductor fabrication, surface engineering, environmental remediation, and energy applications, are difficult to model using conventional computa...

Distinguishing learning asymmetry from perseveration in behavioral models

A statistical test that distinguishes genuine learning asymmetries from spurious effects is introduced and it is found that some previously reported confirmation biases are fragile, albeit others remain robust even at an across-study level.

Self-adaptive Microservice Systems Using Dynamic Software Product Lines and Deep Reinforcement Learning