Topics
Area 1: Analysis of General Policies (Supervisor: Timo P. Gros)
1.1 Simon Ståhlberg, Blai Bonet, and Hector Geffner. Learning general optimal policies with graph neural networks: Expressive power, transparency, and limits.
1.2 Rostislav Horčík and Gustav Šír. Expressiveness of graph neural networks in planning domains.
1.3 Dillon Z Chen, Sylvie Thi´ebaux, and Felipe Trevizan. Learning domain independent heuristics for grounded and lifted planning.
Area 2: Training of General Policies (Supervisor: Daniel Höller)
2.1 Sam Toyer, Felipe Trevizan, Sylvie Thiébaux, and Lexing Xie. Action schema networks: Generalised policies with deep learning.
2.2 Or Rivlin, Tamir Hazan, and Erez Karpas. Generalized planning with deep reinforcement learning.
2.3 Simon Ståhlberg, Blai Bonet, and Hector Geffner. Learning general policies with policy gradient methods.
Area 3: General Policies using Transformers (Supervisor: Nicola Müller)
3.1 Nicola J. Müller, Pablo Sánchez, Jörg Hoffmann, Verena Wolf and Timo P. Gros. Comparing State-of-the-art Graph Neural Networks and Transformers for General Policy Learning.
3.2 Nicholas Rossetti, Massimiliano Tummolo, Alfonso Emilio Gerevini, Luca Putelli, Ivan Serina, Mattia Chiari, and Matteo Olivato. Learning general policies for planning through gpt models.
3.3 Lucas Lehnert, Sainbayar Sukhbaatar, Paul Mcvay, Michael Rabbat, and Yuandong Tian. Beyond A*: Better planning with transformers via search dynamics bootstrapping.