Erwin Walraven

This webpage contains information about my publications and software, but will no longer be updated. More recent information can be found on Google Scholar and LinkedIn.

Publications

Point-Based Value Iteration for Finite-Horizon POMDPs Erwin Walraven and Matthijs T. J. Spaan Journal of Artificial Intelligence Research, vol. 65, pp. 307–341, 2019.
Planning under Uncertainty in Constrained and Partially Observable Environments PhD dissertation, Delft University of Technology, 2019.
Column Generation Algorithms for Constrained POMDPs Erwin Walraven and Matthijs T. J. Spaan Journal of Artificial Intelligence Research, vol. 62, pp. 489–533, 2018.
Bootstrapping LPs in Value Iteration for Multi-Objective and Partially Observable MDPs Diederik M. Roijers, Erwin Walraven and Matthijs T. J. Spaan Proceedings of the 28th Int. Conference on Automated Planning and Scheduling, pp. 218–226, 2018.
Accelerated Vector Pruning for Optimal POMDP Solvers Erwin Walraven and Matthijs T. J. Spaan Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 3672–3678, 2017.
Bounding the Probability of Resource Constraint Violations in Multi-Agent MDPs Frits de Nijs, Erwin Walraven, Mathijs M. de Weerdt and Matthijs T. J. Spaan Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 3562–3568, 2017.
Planning Under Uncertainty for Aggregated Electric Vehicle Charging with Renewable Energy Supply Erwin Walraven and Matthijs T. J. Spaan Proceedings of the 22nd European Conference on Artificial Intelligence, pp. 904–912, 2016.
Traffic flow optimization: A reinforcement learning approach Erwin Walraven, Matthijs T. J. Spaan and Bram Bakker Engineering Applications of Artificial Intelligence, vol. 52, pp. 203–212, 2016.
Planning under Uncertainty for Aggregated Electric Vehicle Charging using Markov Decision Processes Erwin Walraven and Matthijs T. J. Spaan International Workshop on Artificial Intelligence for Smart Grids and Smart Buildings, 2016.
Planning under Uncertainty with Weighted State Scenarios Erwin Walraven and Matthijs T. J. Spaan Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, pp. 912–921, 2015.
A Scenario State Representation for Scheduling Deferrable Loads under Wind Uncertainty Erwin Walraven and Matthijs T. J. Spaan The 10th Annual Workshop on Multiagent Sequential Decision Making Under Uncertainty, 2015.
Traffic Flow Optimization using Reinforcement Learning (abstract) Proceedings of the 26th Benelux Conference on Artificial Intelligence, pp. 211–212, 2014.
Traffic Flow Optimization using Reinforcement Learning Master's thesis, Delft University of Technology, 2014.
Enhancing SAT Based Planning with Landmark Knowledge Jan Elffers, Dyan Konijnenberg, Erwin Walraven and Matthijs T. J. Spaan Proceedings of the 25th Benelux Conference on Artificial Intelligence, pp. 64–71, 2013.

Software

My accelerated version of incremental pruning for POMDPs is available in SolvePOMDP, which is an open source software toolbox containing exact and approximate algorithms for POMDPs.

Publications

Point-Based Value Iteration for Finite-Horizon POMDPs

Planning under Uncertainty in Constrained and Partially Observable Environments

Column Generation Algorithms for Constrained POMDPs

Bootstrapping LPs in Value Iteration for Multi-Objective and Partially Observable MDPs

Accelerated Vector Pruning for Optimal POMDP Solvers

Bounding the Probability of Resource Constraint Violations in Multi-Agent MDPs

Planning Under Uncertainty for Aggregated Electric Vehicle Charging with Renewable Energy Supply

Traffic flow optimization: A reinforcement learning approach

Planning under Uncertainty for Aggregated Electric Vehicle Charging using Markov Decision Processes

Planning under Uncertainty with Weighted State Scenarios

A Scenario State Representation for Scheduling Deferrable Loads under Wind Uncertainty

Traffic Flow Optimization using Reinforcement Learning (abstract)

Traffic Flow Optimization using Reinforcement Learning

Enhancing SAT Based Planning with Landmark Knowledge

Software