reinforcement learning

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...

Compact Reinforcement Learning: Resource-Efficient Formulations for Large-Scale Decision Making

Reinforcement learning (RL) offers a general framework for addressing problems involving sequential decision-making. However, modern practical applications of RL involve prohibitively large state and action sets: such as those in slate recommendations, image-based control, and large language-modeling tasks. Standard fo...

AI-driven whole-life costing for decision-ready UK public building retrofits

Abstract Public-sector building retrofits in the United Kingdom require decisions that balance whole-life cost, carbon reduction, service continuity and auditability under limited data and constrained capital budgets. This paper develops a decision-ready whole-life costing (WLC) workflow that integrates environmental,...

Reinforcement learning enhanced optimal experiment design for inverse problem in elasticity

Prediction-enhanced deep reinforcement learning for RIS-aided high-speed railway security under unknown eavesdropping

From rules to learning: A GIS-integrated agent-based reinforcement learning framework for simulating farmers’ land-use decisions and sustainable policy design