159 citations · 329 across the 29 of their papers we have counts for
4 papers · 1 filter
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…
RRT-CoLearn: towards kinodynamic planning without numerical trajectory optimization
Wouter Wolfslag, Mukunda Bharatheesha, Thomas Moerland +1
Sampling-based kinodynamic planners, such as Rapidly-exploring Random Trees (RRTs), pose two fundamental challenges: computing a reliable (pseudo-)metric for the distance between 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…
Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning
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. We focus on evaluating transition function estimation, while we…