4 citations · 8 across the 3 of their papers we have counts for
7 papers
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…
Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games
Roxana Rădulescu, Timothy Verstraeten, Yijie Zhang +3
Many real-world multi-agent interactions consider multiple distinct criteria, i.e. the payoffs are multi-objective in nature. However, the same multi-objective payoff vector may le…
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…