reinforcement learning

Retraction of “Deep Reinforcement Learning for Structural Optimization and Potential Energy Landscape of 13-Atom Gold Nanoclusters for Application in Nanomaterial Discovery”

WAGRL: A Wind Adaptive Guidance Framework for Uncertainty-Aware UAV Trajectory Control

To address the challenges of wind-induced uncertainty, decision inconsistency, and safety risks in quadrotor trajectory tracking, this study presents a Wind Adaptive Guidance Reinforcement Learning (WAGRL) framework. Unlike conventional controllers relying on fixed aerodynamic models, WAGRL integrates ensemble-based po...

Experimental Research of Hybrid Reinforcement Learning Methods for QoS-Aware Web Service Composition: an Actor-Critic Approach

AI-Based Optimization Models for High-Performance Computing and Intelligent Network Architectures

Artificial Intelligence (AI) has become a necessary technology to optimize High-Performance computers (HPC) systems and intelligent network architecture, such as optimizing the use of resources, the schedulization of workloads and network efficiency. This paper discusses how the three AI-based optimization tools, i.e.,...

FROM TRAJECTORY OPTIMIZATION TO LEARNING: DEVELOPING CONTROLLERS FOR DYNAMIC LEGGED ROBOTS

Legged robots are well suited for cluttered, uneven, and human-centered environments that remain challenging for wheeled systems. However, achieving agile, robust, and efficient locomotion comparable to that of humans and animals in such settings remains difficult due to underactuation, nonlinear hybrid dynamics, and t...

The need for uncertainty: why probabilistic LSTMs are key to improving flood predictions and enabling learned warning rules

Abstract. Deterministic model predictions can struggle to adequately capture extreme events such as floods and droughts, which are of particular relevance in hydrology. This limitation arises because deterministic models collapse the conditional runoff distribution to a single point estimate. Probabilistic modeling pro...

Learning to stabilize nonequilibrium phases of matter with active feedback using partial information

Abstract Recent advances in quantum computing experiments have enabled new pathways for feedback control. Here, we investigate the role of information in interactive quantum many-body dynamics to engineer out-of-equilibrium phases of matter. To this end, we train reinforcement learning agents to prevent entanglement sp...

Reinforcement learning to disentangle multiqubit quantum states from partial observations

Using partial knowledge of a quantum state to control multiqubit entanglement is a largely unexplored paradigm in the emerging field of quantum interactive dynamics with the potential to address outstanding challenges in quantum state preparation and compression, quantum control, and quantum complexity. We present a de...

An Integrated XAI-RL Architecture for Ethical Decision-Making in Autonomous Robots

Can a robot's ethical reasoning be transparent without slowing it down? That is the question we set out to answer. Autonomous robots now make decisions that affect people's lives, and if we cannot understand how they decide, trust becomes a serious problem. We built a unified architecture that couples Explainable AI (X...

STOCHASTIC TRAJECTORY PROCESSES AND GRAPH NEURAL NETWORK LEARNING FOR ADAPTIVE COVERAGE IN UAV NETWORKS

This dissertation develops a framework for UAV trajectory design that bridges theoretical coverage guarantees and practical implementation constraints. The research introduces stochastic trajectory processes as a unifying paradigm, departing from deterministic optimization approaches that require precise user locations...

Towards Realistic Simulated Students: Aligning Language Models with Student Knowledge and Behavior

As large language models (LLMs) are increasingly used in education, there is a growing need to quickly verify and steer LLM-generated content for the benefit of students. In particular, it is important to ensure that educational AI systems improve learning outcomes and engagement while minimizing potential harms. While...

TD-interpreter: enhancing the understanding of timing diagrams with visual-language learning

Abstract We introduce TD-Interpreter, a specialised machine learning (ML) workflow that assists engineers in understanding complex timing diagrams (TDs), originating from a third party, during their design and verification process. The outcome of this workflow is a visual question-answer environment which allows engine...