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Poster
Neuro-algorithmic Policies Enable Fast Combinatorial Generalization
Marin Vlastelica · Michal Rolinek · Georg Martius

Tue Jul 20 09:00 AM -- 11:00 AM (PDT) @ None #None
in Poster Session 1 »

Although model-based and model-free approaches to learning the control of systems have achieved impressive results on standard benchmarks, generalization to task variations is still lacking. Recent results suggest that generalization for standard architectures improves only after obtaining exhaustive amounts of data. We give evidence that generalization capabilities are in many cases bottlenecked by the inability to generalize on the combinatorial aspects of the problem. We show that, for a certain subclass of the MDP framework, this can be alleviated by a neuro-algorithmic policy architecture that embeds a time-dependent shortest path solver in a deep neural network. Trained end-to-end via blackbox-differentiation, this method leads to considerable improvement in generalization capabilities in the low-data regime.

Keywords: Reinforcement Learning and Planning

Author Information

Marin Vlastelica (Max Planck Institute for Intelligent Systems)
Michal Rolinek (Max Planck Institute for Intelligent Systems)
Georg Martius (Max Planck Institute for Intelligent Systems)

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