5 citations · 11 across the 5 of their papers we have counts for
7 papers
Factors of Influence of the Overestimation Bias of Q-Learning
Julius Wagenbach, Matthia Sabatelli
We study whether the learning rate , the discount factor and the reward signal have an influence on the overestimation bias of the Q-Learning algorithm. Our preliminary…
Machine Learning Students Overfit to Overfitting
Matias Valdenegro-Toro, Matthia Sabatelli
Overfitting and generalization is an important concept in Machine Learning as only models that generalize are interesting for general applications. Yet some students have trouble l…
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning
Remo Sasso, Matthia Sabatelli, Marco A. Wiering
Reinforcement learning (RL) is well known for requiring large amounts of data in order for RL agents to learn to perform complex tasks. Recent progress in model-based RL allows age…
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning
Pascal Leroy, Damien Ernst, Pierre Geurts +3
This paper introduces four new algorithms that can be used for tackling multi-agent reinforcement learning (MARL) problems occurring in cooperative settings. All algorithms are bas…
On the Transferability of Winning Tickets in Non-Natural Image Datasets
Matthia Sabatelli, Mike Kestemont, Pierre Geurts
We study the generalization properties of pruned neural networks that are the winners of the lottery ticket hypothesis on datasets of natural images. We analyse their potential und…
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms
Matthia Sabatelli, Gilles Louppe, Pierre Geurts +1
This paper makes one step forward towards characterizing a new family of \textit{model-free} Deep Reinforcement Learning (DRL) algorithms. The aim of these algorithms is to jointly…