159 citations · 329 across the 29 of their papers we have counts for
19 papers · 1 filter
A Unified Framework for Zero-Shot Reinforcement Learning
Jacopo Di Ventura, Jan Felix Kleuker, Aske Plaat +1
Zero-shot reinforcement learning (RL) has emerged as a setting for developing general agents, capable of solving downstream tasks without additional training or planning at test-ti…
Towards a Practical Understanding of Lagrangian Methods in Safe Reinforcement Learning
Lindsay Spoor, Álvaro Serra-Gómez, Aske Plaat +1
Safe reinforcement learning addresses constrained optimization problems where maximizing performance must be balanced against safety constraints, and Lagrangian methods are a widel…
On the Effect of Regularization in Policy Mirror Descent
Jan Felix Kleuker, Aske Plaat, Thomas Moerland
Policy Mirror Descent (PMD) has emerged as a unifying framework in reinforcement learning (RL) by linking policy gradient methods with a first-order optimization method known as mi…
Chargax: A JAX Accelerated EV Charging Simulator
Koen Ponse, Jan Felix Kleuker, Aske Plaat +1
Deep Reinforcement Learning can play a key role in addressing sustainable energy challenges. For instance, many grid systems are heavily congested, highlighting the urgent need to…
Reinforcement Learning for Sustainable Energy: A Survey
Koen Ponse, Felix Kleuker, Márton Fejér +3
The transition to sustainable energy is a key challenge of our time, requiring modifications in the entire pipeline of energy production, storage, transmission, and consumption. At…
EduGym: An Environment and Notebook Suite for Reinforcement Learning Education
Thomas M. Moerland, Matthias Müller-Brockhausen, Zhao Yang +7
Due to the empirical success of reinforcement learning, an increasing number of students study the subject. However, from our practical teaching experience, we see students enterin…
Are LSTMs Good Few-Shot Learners?
Mike Huisman, Thomas M. Moerland, Aske Plaat +1
Deep learning requires large amounts of data to learn new tasks well, limiting its applicability to domains where such data is available. Meta-learning overcomes this limitation by…
What model does MuZero learn?
Jinke He, Thomas M. Moerland, Joery A. de Vries +1
Model-based reinforcement learning (MBRL) has drawn considerable interest in recent years, given its promise to improve sample efficiency. Moreover, when using deep-learned models,…
Two-Memory Reinforcement Learning
Zhao Yang, Thomas. M. Moerland, Mike Preuss +1
While deep reinforcement learning has shown important empirical success, it tends to learn relatively slow due to slow propagation of rewards information and slow update of paramet…
First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivation
Zhao Yang, Thomas M. Moerland, Mike Preuss +1
Go-Explore achieved breakthrough performance on challenging reinforcement learning (RL) tasks with sparse rewards. The key insight of Go-Explore was that successful exploration req…
Continuous Episodic Control
Zhao Yang, Thomas M. Moerland, Mike Preuss +1
Non-parametric episodic memory can be used to quickly latch onto high-rewarded experience in reinforcement learning tasks. In contrast to parametric deep reinforcement learning app…
When to Go, and When to Explore: The Benefit of Post-Exploration in Intrinsic Motivation
Zhao Yang, Thomas M. Moerland, Mike Preuss +1
Go-Explore achieved breakthrough performance on challenging reinforcement learning (RL) tasks with sparse rewards. The key insight of Go-Explore was that successful exploration req…
On Credit Assignment in Hierarchical Reinforcement Learning
Joery A. de Vries, Thomas M. Moerland, Aske Plaat
Hierarchical Reinforcement Learning (HRL) has held longstanding promise to advance reinforcement learning. Yet, it has remained a considerable challenge to develop practical algori…
Visualizing MuZero Models
Joery A. de Vries, Ken S. Voskuil, Thomas M. Moerland +1
MuZero, a model-based reinforcement learning algorithm that uses a value equivalent dynamics model, achieved state-of-the-art performance in Chess, Shogi and the game of Go. In con…
Model-based Reinforcement Learning: A Survey
Thomas M. Moerland, Joost Broekens, Aske Plaat +1
Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is a important challenge in artificial intelligence. Two key approaches to this probl…
A Unifying Framework for Reinforcement Learning and Planning
Thomas M. Moerland, Joost Broekens, Aske Plaat +1
Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a key challenge in artificial intelligence. Two successful approaches to MDP optimi…
The Potential of the Return Distribution for Exploration in RL
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. We study network losses and propagation mecha…
Efficient exploration with Double Uncertain Value Networks
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. We identify two sources of uncertainty t…
Emotion in Reinforcement Learning Agents and Robots: A Survey
Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker
This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores huma…