19 citations · 43 across the 5 of their papers we have counts for
6 papers
Online Planning in POMDPs with State-Requests
Raphael Avalos, Eugenio Bargiacchi, Ann Nowé +2
In key real-world problems, full state information is sometimes available but only at a high cost, like activating precise yet energy-intensive sensors or consulting humans, thereb…
Pareto Conditioned Networks
Mathieu Reymond, Eugenio Bargiacchi, Ann Nowé
In multi-objective optimization, learning all the policies that reach Pareto-efficient solutions is an expensive process. The set of optimal policies can grow exponentially with th…
A Practical Guide to Multi-Objective Reinforcement Learning and Planning
Conor F. Hayes, Roxana Rădulescu, Eugenio Bargiacchi +15
Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcemen…
Scalable Optimization for Wind Farm Control using Coordination Graphs
Timothy Verstraeten, Pieter-Jan Daems, Eugenio Bargiacchi +3
Wind farms are a crucial driver toward the generation of ecological and renewable energy. Due to their rapid increase in capacity, contemporary wind farms need to adhere to strict…
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping
Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers +1
We present a new model-based reinforcement learning algorithm, Cooperative Prioritized Sweeping, for efficient learning in multi-agent Markov decision processes. The algorithm allo…
Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood Structures
Timothy Verstraeten, Eugenio Bargiacchi, Pieter JK Libin +3
Multi-agent coordination is prevalent in many real-world applications. However, such coordination is challenging due to its combinatorial nature. An important observation in this r…