1 code implementation • 18 Jan 2024 • Riccardo Majellaro, Jonathan Collu, Aske Plaat, Thomas M. Moerland
Extracting structured representations from raw visual data is an important and long-standing challenge in machine learning.
1 code implementation • 17 Nov 2023 • Thomas M. Moerland, Matthias Müller-Brockhausen, Zhao Yang, Andrius Bernatavicius, Koen Ponse, Tom Kouwenhoven, Andreas Sauter, Michiel van der Meer, Bram Renting, Aske Plaat
To solve this issue we introduce EduGym, a set of educational reinforcement learning environments and associated interactive notebooks tailored for education.
1 code implementation • 22 Oct 2023 • Mike Huisman, Thomas M. Moerland, Aske Plaat, Jan N. van Rijn
Meta-learning overcomes this limitation by learning how to learn.
no code implementations • 1 Jun 2023 • Jinke He, Thomas M. Moerland, Frans A. Oliehoek
Model-based reinforcement learning has drawn considerable interest in recent years, given its promise to improve sample efficiency.
no code implementations • 6 Dec 2022 • Zhao Yang, Thomas M. Moerland, Mike Preuss, Aske Plaat
In this paper, we present a clear ablation study of post-exploration in a general intrinsically motivated goal exploration process (IMGEP) framework, that the Go-Explore paper did not show.
no code implementations • 28 Nov 2022 • Zhao Yang, Thomas M. Moerland, Mike Preuss, Aske Plaat
Therefore, this paper introduces Continuous Episodic Control (CEC), a novel non-parametric episodic memory algorithm for sequential decision making in problems with a continuous action space.
no code implementations • 29 Mar 2022 • Zhao Yang, Thomas M. Moerland, Mike Preuss, Aske Plaat
Go-Explore achieved breakthrough performance on challenging reinforcement learning (RL) tasks with sparse rewards.
1 code implementation • 7 Mar 2022 • Joery A. de Vries, Thomas M. Moerland, Aske Plaat
To improve our fundamental understanding of HRL, we investigate hierarchical credit assignment from the perspective of conventional multistep reinforcement learning.
Hierarchical Reinforcement Learning reinforcement-learning +1
1 code implementation • ICML Workshop URL 2021 • Joery A. de Vries, Ken S. Voskuil, Thomas M. Moerland, Aske Plaat
In contrast to standard forward dynamics models that predict a full next state, value equivalent models are trained to predict a future value, thereby emphasizing value relevant information in the representations.
no code implementations • 30 Jun 2020 • Thomas M. Moerland, Joost Broekens, Aske Plaat, Catholijn M. Jonker
Two key approaches to this problem are reinforcement learning (RL) and planning.
no code implementations • 26 Jun 2020 • Thomas M. Moerland, Joost Broekens, Aske Plaat, Catholijn M. Jonker
Therefore, this paper presents a unifying algorithmic framework for reinforcement learning and planning (FRAP), which identifies underlying dimensions on which MDP planning and learning algorithms have to decide.
1 code implementation • 19 May 2020 • Thomas M. Moerland, Joost Broekens, Aske Plaat, Catholijn M. Jonker
Monte Carlo Tree Search (MCTS) efficiently balances exploration and exploitation in tree search based on count-derived uncertainty.
1 code implementation • 15 May 2020 • Thomas M. Moerland, Anna Deichler, Simone Baldi, Joost Broekens, Catholijn M. Jonker
Planning and reinforcement learning are two key approaches to sequential decision making.
1 code implementation • 11 Jun 2018 • Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker
This paper studies the potential of the return distribution for exploration in deterministic reinforcement learning (RL) environments.
2 code implementations • 24 May 2018 • Thomas M. Moerland, Joost Broekens, Aske Plaat, Catholijn M. Jonker
A core novelty of Alpha Zero is the interleaving of tree search and deep learning, which has proven very successful in board games like Chess, Shogi and Go.
2 code implementations • 23 May 2018 • Thomas M. Moerland, Joost Broekens, Aske Plaat, Catholijn M. Jonker
Asymmetric termination of search trees introduces a type of uncertainty for which the standard upper confidence bound (UCB) formula does not account.
no code implementations • 29 Nov 2017 • Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker
This paper studies directed exploration for reinforcement learning agents by tracking uncertainty about the value of each available action.
no code implementations • 15 May 2017 • Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker
This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents.
1 code implementation • 1 May 2017 • Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker
In this paper we study how to learn stochastic, multimodal transition dynamics in reinforcement learning (RL) tasks.
Model-based Reinforcement Learning reinforcement-learning +2