22 papers
Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data
Hannes Nilsson, Rafael Basso, Balázs Kulcsár +1
In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the different sources of energy losses du…
Adaptive Prior Selection in Gaussian Process Bandits with Thompson Sampling
Jack Sandberg, Morteza Haghir Chehreghani
Gaussian process (GP) bandits provide a powerful framework for performing blackbox optimization of unknown functions. The characteristics of the unknown function depend heavily on…
Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic
Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani
Balancing safety, efficiency, and operational costs in highway driving poses a challenging decision-making problem for heavy-duty vehicles. A central difficulty is that conventiona…
Interactive Trajectory Planning with Learning-based Distributionally Robust Model Predictive Control and Markov Systems
Erik Börve, Nikolce Murgovski, Morteza Haghir Chehreghani +1
We investigate interactive trajectory planning subject to uncertainty in the decisions of surrounding agents. To control the ego-agent, we aim to first learn the decision distribut…
Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients
Linus Aronsson, Morteza Haghir Chehreghani
Active feature acquisition (AFA) considers prediction problems in which features are costly to obtain and the learner adaptively decides which feature values to acquire for each in…
Two-Stage Learned Decomposition for Scalable Routing on Multigraphs
Filip Rydin, Morteza Haghir Chehreghani, Balázs Kulcsár
Most neural methods for Vehicle Routing Problems (VRPs) are limited to Euclidean settings or simple graphs. In this work, we instead consider multigraphs, where parallel edges repr…