Know what it does not know: Improving Offline Deep Reinforcement Learning with Uncertainty Estimation

Offline reinforcement learning, or learning from a fixed data set, is an attractive alternative to online reinforcement learning. Offline reinforcement learning promises to address the cost and safety implications of taking numerous random or bad actions online, which is a crucial aspect of traditional reinforcement learning that makes it...

PEBL: Pessimistic Ensembles for Offline Deep Reinforcement Learning

Offline reinforcement learning (RL), or learning from a fixed data set, is an attractive alternative to online RL. Offline RL promises to address the cost and safety implications of tak- ing numerous random or bad actions online, a crucial aspect of traditional RL that makes it difficult to apply in real-world problems. However, when RL is na...

OffSide: Learning to Identify Mistakes in Boundary Conditions

Mistakes in boundary conditions are the cause of many bugs in software. These mistakes happen when, e.g., developers make use of '<' or '>' in cases where they should have used '<=' or '>='. Mistakes in boundary conditions are often hard to find and manually detecting them might be very time-consuming for developers. While...

Developing a Platform for Traffic Data Analysis

Scenwise is a business working on innovative and sophisticated solutions in the domain of traffic management. Leveraging data science and IT systems, Scenwise delivers products to institutions to facilitate efficient traffic management. In order to manage the highly complex network of infrastructure on the road network, traffic managers need to...