David Kuric’s paper on learning re-useable options was accepted to TMLR. Paper, code, and video are available on OpenReview. It proposes a gradient-based meta learning approach to discover sub-policies that are useful for rapid adaptation to different MDPs in a family of tasks. Congrats, David!
Postdoc position on learning an reasoning for medical decision making
At the VU Amsterdam, there are two open postdoc positions on decision making in the medical domain. In one of them, I will be involved as co-advisor. For all details and how to apply, please check the official vacancy.
ICLR paper accepted
Qi and Marco’s paper got accept for presentation at ICLR. Congratulations! The submitted version is available here, camera-ready version is coming soon.
Neurips papers
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.
ICML paper accepted
Two IJCAI papers accepted
Two of our IJCAI submissions were accepted:
With Niklas Höpner & Ilaria Tiddi: Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods (pre-print)
With Jan Wöhlke & Felix Schmitt: Value Refinement Network (VRN) (earlier version)
Congrats Jan & Niklas!
New working paper on class abstractions in RL
Niklas Höpner has recently posted his working paper on leveraging class abstraction for commonsense reinforcement learning. It is available on the arXiv.
CPAIOR paper accepted
Wouter Kool’s paper “Deep policy dynamic programming for vehicle routing problems” was accepted to CPAIOR. Congratulations! A preprint is available on the arXiv.
ICLR paper accepted
AAAI paper accepted
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!