reinforcement learning

Deep Reinforcement Learning-Based Arbitrary Beam Pattern Synthesis for Millimeter-Wave Reconfigurable Intelligent Surfaces With Discrete Phase Shifts

Abstract Millimeter-wave reconfigurable intelligent surface (RIS) panels restricted to 1bit or 2-bit discrete phase shifts must synthesize arbitrary beam patterns from a solution space that grows exponentially with the number of reflecting elements. Existing two-dimensional finite-impulse-response filter analogies comp...

Robust high-fidelity three-qubit gates design for superconducting quantum computers via model-free reinforcement learning

Multi-qubit gates are crucial to realizing quantum algorithms. Direct implementation of these gates will facilitate a reduction in circuit depth. However, existing model-based methods rely on precise characterization of the system Hamiltonian as well as execution of the control fields, which present challenges in obt...

Adaptive Switching between Parallel-World Self-Reinforcement and Mutual Reinforcement for Multiple Large Language Models

Large language models (LLMs) can be improved through self-generated feedback, reward-guided optimization, self-play, and interactions among multiple agents. However, a system that consistently relies on one reinforcement mode may fail to exploit complementary learning opportunities. Self-reinforcement enables an indivi...

Sustainable AI in and for space robotics: A review

Ethics-Aware Hybrid Graph–Neuro-Symbolic Framework for Adaptive Fraud Management in Large-Scale Financial Systems

Fraud management systems are becoming more and more based on data-driven artificial intelligence processes to identify anomalous and malicious behavior in massive digital ecosystems. Nevertheless, the current solutions are usually characterized by low flexibility, inability to be interpreted, and such ethical limitatio...

An analytical database for reinforced concrete structural walls based on an experimentally validated ML-integrated modeling framework

Experimental datasets of reinforced concrete (RC) structural walls remain limited in both size and parameter diversity, leaving important regions of the design space underrepresented and constraining the development of reliable predictive models. To address these limitations, this study develops an analytical database...

Distributional Bellman Operators with Power-Law Horizon Weights: Contraction and Fixed-Point Analysis

OTTP: A Benchmark Protocol for Trajectory-Prefix Risk Prediction in Offline Safe Reinforcement Learning

OTTP provides a reproducible protocol and benchmark for hindsight-defined trajectory-prefix cumulative-cost prediction on offline safe-RL logs. The primary endpoint is Step catastrophic AUPRC. The archive contains code, result tables, run records, and split and preprocessing artifacts.

System design of the Ultra Mobility Vehicle: A driving, balancing, and jumping bicycle robot

Trials cyclists and mountain bike riders can hop, jump, balance, and drive on one or both wheels. This versatility allows them to achieve speed and energy efficiency on smooth terrain and agility over rough terrain. Inspired by these athletes, we present the design and control of a robotic platform, Ultra Mobility Vehi...

Clustering of differential conditioned response trajectories using skin conductance: Are fear conditioning studies affected by pre-existing characteristics?

Recent research suggests that responses during experimental fear conditioning follow distinct latent trajectory classes. Computational reinforcement learning models can be used to infer latent learning processes during the task (e.g., learning rates and prediction-error sensitivity) that may help explain these classes....

Graph-based reinforcement learning framework with control barrier functions for safe marine traffic control

Centralized control that optimizes ship behavior is an effective approach for managing marine traffic in congested waters. This study proposed a decision-making framework that generates optimal control actions for all ships in a sea area using traffic data represented as a graph. In this framework, control actions that...

Charting the Trajectory of Artificial Intelligence Agents in Air Traffic Control

The aim of the study was to chart the evolution and the current research landscape of artificial intelligence (AI) agents within the domain of air traffic control. Using bibliometric analysis, the study identified major research trends and gaps and provided a consolidated reference for researchers. The analysis include...