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arXiv · 2201.13388

Compositional Multi-Object Reinforcement Learning with Linear Relation Networks

Davide Mambelli·Frederik Träuble·Stefan Bauer·Bernhard Schölkopf·Francesco Locatello
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Abstract

Although reinforcement learning has seen remarkable progress over the last years, solving robust dexterous object-manipulation tasks in multi-object settings remains a challenge. In this paper, we focus on models that can learn manipulation tasks in fixed multi-object settings and extrapolate this skill zero-shot without any drop in performance when the number of objects changes. We consider the generic task of bringing a specific cube out of a set to a goal position. We find that previous approaches, which primarily leverage attention and graph neural network-based architectures, do not generalize their skills when the number of input objects changes while scaling as $K^2$. We propose an alternative plug-and-play module based on relational inductive biases to overcome these limitations. Besides exceeding performances in their training environment, we show that our approach, which scales linearly in $K$, allows agents to extrapolate and generalize zero-shot to any new object number.

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Davide Mambelli, Frederik Träuble, Stefan Bauer, Bernhard Schölkopf, Francesco Locatello. 2022-01-31. Compositional Multi-Object Reinforcement Learning with Linear Relation Networks. https://arxiv.org/abs/2201.13388

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