Search Results for author: Timothy Verstraeten

Found 13 papers, 7 papers with code

Evaluating COVID-19 vaccine allocation policies using Bayesian $m$-top exploration

1 code implementation30 Jan 2023 Alexandra Cimpean, Timothy Verstraeten, Lander Willem, Niel Hens, Ann Nowé, Pieter Libin

$m$-top exploration allows the algorithm to learn $m$ policies for which it expects the highest utility, enabling experts to inspect this small set of alternative strategies, along with their quantified uncertainty.

Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games

1 code implementation14 Nov 2020 Roxana Rădulescu, Timothy Verstraeten, Yijie Zhang, Patrick Mannion, Diederik M. Roijers, Ann Nowé

We contribute novel actor-critic and policy gradient formulations to allow reinforcement learning of mixed strategies in this setting, along with extensions that incorporate opponent policy reconstruction and learning with opponent learning awareness (i. e., learning while considering the impact of one's policy when anticipating the opponent's learning step).

Deep reinforcement learning for large-scale epidemic control

1 code implementation30 Mar 2020 Pieter Libin, Arno Moonens, Timothy Verstraeten, Fabian Perez-Sanjines, Niel Hens, Philippe Lemey, Ann Nowé

For this reason, we investigate a deep reinforcement learning approach to automatically learn prevention strategies in the context of pandemic influenza.

Computational Efficiency reinforcement-learning +1

Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping

no code implementations15 Jan 2020 Eugenio Bargiacchi, Timothy Verstraeten, Diederik M. Roijers, Ann Nowé

We present a new model-based reinforcement learning algorithm, Cooperative Prioritized Sweeping, for efficient learning in multi-agent Markov decision processes.

Model-based Reinforcement Learning Multi-agent Reinforcement Learning +3

Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood Structures

1 code implementation22 Nov 2019 Timothy Verstraeten, Eugenio Bargiacchi, Pieter JK Libin, Jan Helsen, Diederik M. Roijers, Ann Nowé

In this task, wind turbines must coordinate their alignments with respect to the incoming wind vector in order to optimize power production.

Thompson Sampling

Fleet Control using Coregionalized Gaussian Process Policy Iteration

1 code implementation22 Nov 2019 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.

Gaussian Processes reinforcement-learning +2

IPC-Net: 3D point-cloud segmentation using deep inter-point convolutional layers

no code implementations30 Sep 2019 Felipe Gomez Marulanda, Pieter Libin, Timothy Verstraeten, Ann Nowé

In general, our approach outperforms PointNet on every family of 3D geometries on which the models were tested.

Point Cloud Segmentation

Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies

no code implementations16 Nov 2017 Pieter Libin, Timothy Verstraeten, Diederik M. Roijers, Jelena Grujic, Kristof Theys, Philippe Lemey, Ann Nowé

We evaluate these algorithms in a realistic experimental setting and demonstrate that it is possible to identify the optimal strategy using only a limited number of model evaluations, i. e., 2-to-3 times faster compared to the uniform sampling method, the predominant technique used for epidemiological decision making in the literature.

Decision Making Thompson Sampling

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