159 citations · 342 across the 21 of their papers we have counts for
6 papers · 1 filter
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…
Optimistic Risk Perception in the Temporal Difference error Explains the Relation between Risk-taking, Gambling, Sensation-seeking and Low Fear
Joost Broekens, Tim Baarslag
Understanding the affective, cognitive and behavioural processes involved in risk taking is essential for treatment and for setting environmental conditions to limit damage. Using…