15 citations · 25 across the 18 of their papers we have counts for
18 papers
Maximising the Set-Piece Return: Optimising Football Corner Tactics with Graph Reinforcement Learning
Sean Groom, Michael Groom, Francisco Belo +4
Machine learning is increasingly employed for the evaluation of football tactics. However, existing approaches focus on characterising historical actions or analyst-specified count…
Online Navigation Planning for Long-term Autonomous Operation of Underwater Gliders
Victor-Alexandru Darvariu, Charlotte Z. Reed, Jan Stratmann +21
Underwater glider robots have become indispensable for ocean sampling, yet fully autonomous long-term operation remains rare in practice. Although stakeholders are calling for tool…
JaxWildfire: A GPU-Accelerated Wildfire Simulator for Reinforcement Learning
Ufuk Çakır, Victor-Alexandru Darvariu, Bruno Lacerda +1
Artificial intelligence methods are increasingly being explored for managing wildfires and other natural hazards. In particular, reinforcement learning (RL) is a promising path tow…
Gaussian Process Aggregation for Root-Parallel Monte Carlo Tree Search with Continuous Actions
Junlin Xiao, Victor-Alexandru Darvariu, Bruno Lacerda +1
Monte Carlo Tree Search is a cornerstone algorithm for online planning, and its root-parallel variant is widely used when wall clock time is limited but best performance is desired…
Accelerating Atomic Fine Structure Determination with Graph Reinforcement Learning
M. Ding, V. -A. Darvariu, A. N. Ryabtsev +2
Atomic data determined by analysis of observed atomic spectra are essential for plasma diagnostics. For each low-ionisation open d- and f-subshell atomic species, around fin…
Tackling GNARLy Problems: Graph Neural Algorithmic Reasoning Reimagined through Reinforcement Learning
Alex Schutz, Victor-Alexandru Darvariu, Efimia Panagiotaki +2
Neural algorithmic reasoning (NAR) is a paradigm that trains neural networks to execute classic algorithms by supervised learning. Despite its successes, important limitations rema…
Reinforcement Learning Discovers Efficient Decentralized Graph Path Search Strategies
Alexei Pisacane, Victor-Alexandru Darvariu, Mirco Musolesi
Graph path search is a classic computer science problem that has been recently approached with Reinforcement Learning (RL) due to its potential to outperform prior methods. Existin…
Large Language Models are Effective Priors for Causal Graph Discovery
Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Causal structure discovery from observations can be improved by integrating background knowledge provided by an expert to reduce the hypothesis space. Recently, Large Language Mode…
Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective
Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Graphs are a natural representation for systems based on relations between connected entities. Combinatorial optimization problems, which arise when considering an objective functi…
Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery
Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Identifying causal structure is central to many fields ranging from strategic decision-making to biology and economics. In this work, we propose CD-UCT, a model-based reinforcement…
Graph Reinforcement Learning for Operator Selection in the ALNS Metaheuristic
Syu-Ning Johnn, Victor-Alexandru Darvariu, Julia Handl +1
ALNS is a popular metaheuristic with renowned efficiency in solving combinatorial optimisation problems. However, despite 16 years of intensive research into ALNS, whether the embe…
Graph Neural Modeling of Network Flows
Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Network flow problems, which involve distributing traffic such that the underlying infrastructure is used effectively, are ubiquitous in transportation and logistics. Among them, t…
Dynamic Network Reconfiguration for Entropy Maximization using Deep Reinforcement Learning
Christoffel Doorman, Victor-Alexandru Darvariu, Stephen Hailes +1
A key problem in network theory is how to reconfigure a graph in order to optimize a quantifiable objective. Given the ubiquity of networked systems, such work has broad practical…
Trust-based Consensus in Multi-Agent Reinforcement Learning Systems
Ho Long Fung, Victor-Alexandru Darvariu, Stephen Hailes +1
An often neglected issue in multi-agent reinforcement learning (MARL) is the potential presence of unreliable agents in the environment whose deviations from expected behavior can…
Reinforcement Learning on Encrypted Data
Alberto Jesu, Victor-Alexandru Darvariu, Alessandro Staffolani +2
The growing number of applications of Reinforcement Learning (RL) in real-world domains has led to the development of privacy-preserving techniques due to the inherently sensitive…
Planning Spatial Networks with Monte Carlo Tree Search
Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
We tackle the problem of goal-directed graph construction: given a starting graph, a budget of modifications, and a global objective function, the aim is to find a set of edges who…
Solving Graph-based Public Good Games with Tree Search and Imitation Learning
Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Public goods games represent insightful settings for studying incentives for individual agents to make contributions that, while costly for each of them, benefit the wider society.…
Goal-directed graph construction using reinforcement learning
Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi
Graphs can be used to represent and reason about systems and a variety of metrics have been devised to quantify their global characteristics. However, little is currently known abo…