Kristof Van Moffaert

According to our database1, Kristof Van Moffaert authored at least 13 papers between 2013 and 2015.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.



In proceedings 
PhD thesis 




A Policy Gradient with Parameter-Based Exploration Approach for Zone-Heating.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2015

Risk-sensitivity through multi-objective reinforcement learning.
Proceedings of the IEEE Congress on Evolutionary Computation, 2015

SCANERGY: a Scalable and Modular System for Energy Trading Between Prosumers.
Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, 2015

Multi-objective reinforcement learning using sets of pareto dominating policies.
J. Mach. Learn. Res., 2014

NRG-X-Change - A Novel Mechanism for Trading of Renewable Energy in Smart Grids.
Proceedings of the SMARTGREENS 2014, 2014

Multi-objective χ-Armed bandits.
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning.
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

Adaptive objective selection for correlated objectives in multi-objective reinforcement learning.
Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2014

Networks as a tool to save energy while keeping up general user comfort in buildings.
Proceedings of the 19th IEEE Workshop on Local & Metropolitan Area Networks, 2013

On the Behaviour of Scalarization Methods for the Engagement of a Wet Clutch.
Proceedings of the 12th International Conference on Machine Learning and Applications, 2013

Reinforcement Learning for Multi-purpose Schedules.
Proceedings of the ICAART 2013, 2013

Hypervolume-Based Multi-Objective Reinforcement Learning.
Proceedings of the Evolutionary Multi-Criterion Optimization, 2013

Scalarized multi-objective reinforcement learning: Novel design techniques.
Proceedings of the 2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning, 2013