95 citations · 184 across the 32 of their papers we have counts for
4 papers · 1 filter
How do Offline Measures for Exploration in Reinforcement Learning behave?
Jakob J. Hollenstein, Sayantan Auddy, Matteo Saveriano +2
Sufficient exploration is paramount for the success of a reinforcement learning agent. Yet, exploration is rarely assessed in an algorithm-independent way. We compare the behavior…
Improving the Exploration of Deep Reinforcement Learning in Continuous Domains using Planning for Policy Search
Jakob J. Hollenstein, Erwan Renaudo, Matteo Saveriano +1
Local policy search is performed by most Deep Reinforcement Learning (D-RL) methods, which increases the risk of getting trapped in a local minimum. Furthermore, the availability o…
Reconfigurable Behavior Trees: Towards an Executive Framework Meeting High-level Decision Making and Control Layer Features
Pilar de la Cruz, Justus Piater, Matteo Saveriano
Behavior Trees constitute a widespread AI tool which has been successfully spun out in robotics. Their advantages include simplicity, modularity, and reusability of code. However,…
Evaluating the Progress of Deep Learning for Visual Relational Concepts
Sebastian Stabinger, Peer David, Justus Piater +1
Convolutional Neural Networks (CNNs) have become the state of the art method for image classification in the last ten years. Despite the fact that they achieve superhuman classific…