19 citations · 40 across the 5 of their papers we have counts for
6 papers
Model-predictive control and reinforcement learning in multi-energy system case studies
Glenn Ceusters, Román Cantú Rodríguez, Alberte Bouso García +6
Model-predictive-control (MPC) offers an optimal control technique to establish and ensure that the total operation cost of multi-energy systems remains at a minimum while fulfilli…
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…
Deep reinforcement learning for large-scale epidemic control
Pieter Libin, Arno Moonens, Timothy Verstraeten +4
Epidemics of infectious diseases are an important threat to public health and global economies. Yet, the development of prevention strategies remains a challenging process, as epid…
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…
Fleet Control using Coregionalized Gaussian Process Policy Iteration
Timothy Verstraeten, Pieter JK Libin, Ann Nowé
In many settings, as for example wind farms, multiple machines are instantiated to perform the same task, which is called a fleet. The recent advances with respect to the Internet…
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…