Marco Wiering

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Marco Alexander Wiering, a Dutch mathematician, computer scientist, and assistant professor at Faculty of Mathematics and Natural Sciences, artificial intelligence and cognitive engineering, University of Groningen with tenure track for associate professor, and until September 2007 assistant professor at Utrecht University. He holds a Ph.D. on the topic of reinforcement learning from University of Amsterdam in 1999, thesis advisors were Frans Groen and Jürgen Schmidhuber. His research interests include artificial intelligence, machine learning, neural networks, object recognition, pattern recognition, evolutionary computation, robotics, game playing, multi-agent systems, time-series analysis and computer vision.

=Selected Publications=

1995 ...

 * Marco Wiering (1995). Learning of Game Evaluation Functions with Hierarchical Neural Architectures. Master's thesis, University of Amsterdam, pdf
 * Marco Wiering, Jürgen Schmidhuber (1997). HQ-learning. Adaptive Behavior, Vol. 6, No 2
 * Marco Wiering, Jürgen Schmidhuber (1998). Fast online Q (λ). Machine Learning, Vol. 33, No. 1
 * Marco Wiering (1999). Explorations in Efficient Reinforcement Learning. Ph.D. thesis, University of Amsterdam, advisors Frans Groen and Jürgen Schmidhuber

2000 ...

 * Henk Mannen, Marco Wiering (2004). Learning to play chess using TD(λ)-learning with database games. Cognitive Artiﬁcial Intelligence, Utrecht University, Benelearn’04, pdf
 * Jan Peter Patist, Marco Wiering (2004). Learning to Play Draughts using Temporal Difference Learning with Neural Networks and Databases. Cognitive Artiﬁcial Intelligence, Utrecht University, Benelearn’04

2005 ...

 * Marco Wiering, Jan Peter Patist, Henk Mannen (2005). Learning to Play Board Games using Temporal Difference Methods. Technical Report, Utrecht University, UU-CS-2005-048, pdf
 * Marco Wiering (2005). QV (λ)-learning: A new on-policy reinforcement learning algorithm. Proceedings of the 7th European Workshop on Reinforcement Learning, pdf

2010 ...

 * Marco Wiering (2010). Self-play and using an expert to learn to play backgammon with temporal difference learning. Journal of Intelligent Learning Systems and Applications, Vol. 2, No. 2
 * Marco Wiering, Martijn Van Otterlo (eds.) (2012). Reinforcement learning: State-of-the-art. Adaptation, Learning, and Optimization, Vol. 12, Springer
 * Sjoerd van den Dries, Marco Wiering (2012). Neural-fitted TD-leaf learning for playing Othello with structured neural networks. IEEE Transactions on Neural Networks and Learning Systems, Vol. 23, No. 11
 * Michiel van der Ree, Marco Wiering (2013). Reinforcement Learning in the Game of Othello: Learning Against a Fixed Opponent and Learning from Self-Play. ADPRL 2013
 * Luuk Bom, Ruud Henken, Marco Wiering (2013). Reinforcement Learning to Train Ms. Pac-Man Using Higher-order Action-relative Inputs. ADPRL 2013

2015 ...

 * Matthia Sabatelli, Francesco Bidoia, Valeriu Codreanu, Marco Wiering (2018). Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead. ICPRAM 2018, pdf
 * Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco Wiering (2018). Deep Quality-Value (DQV) Learning. arXiv:1810.00368
 * Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco Wiering (2019). Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms. arXiv:1909.01779

2020 ...

 * Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco Wiering (2020). The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms. IJCNN 2020

=External Links=
 * Marco Wiering's Home Page
 * Marco Wiering | Universität Groningen
 * M.A. Wiering | Utrecht University
 * Lifeboat Foundation Bios: Dr. Marco A. Wiering
 * The Mathematics Genealogy Project - Marco Wiering
 * Marco Wiering - Google Scholar Citations

=References= Up one level