Compositional Multi-Object Reinforcement Learning with Linear Relation Networks
This addresses the problem of robust dexterous object manipulation for AI agents in multi-object settings, offering a novel solution to improve scalability and generalization.
The paper tackles the challenge of multi-object manipulation in reinforcement learning by proposing a plug-and-play module based on relational inductive biases, which scales linearly in the number of objects and enables zero-shot generalization to varying object counts without performance drop.
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.