This week, Qi Wang is presenting his paper on meta-learning mixture-of-expert neural processes and Mukul Gagrani and Corrado Rainone their paper on neural topological ordering for computation graphs at NeurIPS. Furthermore, Matthew Macfarlane will present preliminary work on learning tree-based policies in the Deep Reinforcement Learning workshop.
Niklas Höpner has recently posted his working paper on leveraging class abstraction for commonsense reinforcement learning. It is available on the arXiv.
Wouter Kool’s paper “Deep policy dynamic programming for vehicle routing problems” was accepted to CPAIOR. Congratulations! A preprint is available on the arXiv.
Alex Long’s AAAI submission on Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation got accepted for presentation! The paper is the result of a collaboration started when Alex visited us at UvA. Congrats, Alex!
We’ve recently posted several working papers that I hope you’ll find interesting:
- Amin, S., Gomrokchi, M., Satija, H., van Hoof, H., & Precup, D. (2021). A Survey of Exploration Methods in Reinforcement Learning. arXiv preprint arXiv:2109.00157.
- Kool, W., van Hoof, H., Gromicho, J., & Welling, M. (2021). Deep Policy Dynamic Programming for Vehicle Routing Problems. arXiv preprint arXiv:2102.11756.
Jan Woehlke’s paper “Hierarchies of Planning and Reinforcement Learning for Robot Navigation” was accepted for presentation at the International Conference on Robotics and Automation.
Yijie Zhang got his paper “Deep Coherent Exploration For Continuous Control” accepted for presentation in the International Conference on Machine Learning.
Congrats Yijie & Jan! Links to the papers / preprint will follow soon on the publication page.
Our lab is recruiting two new assistant professors in machine learning (any subfield, e.g. probabilistic programming, ML for or using combinatorial optimization, RL, …). More info on our institutes vacancy page.