Equivariant $Q$ Learning in Spatial Action Spaces

Abstract

Recently, a variety of new equivariant neural network model architectures have been proposed that generalize better over rotational and reflectional symmetries than standard models. These models are relevant to robotics because many robotics problems can be expressed in a rotationally symmetric way. This paper focuses on equivariance over a visual state space and a spatial action space -- the setting where the robot action space includes a subset of $\mathrm{SE}(2)$. In this situation, we know a priori that rotations and translations in the state image should result in the same rotations and translations in the spatial action dimensions of the optimal policy. Therefore, we can use equivariant model architectures to make $Q$ learning more sample efficient. This paper identifies when the optimal $Q$ function is equivariant and proposes $Q$ network architectures for this setting. We show experimentally that this approach outperforms standard methods in a set of challenging manipulation problems.

arXiv commentAccepted at Conference on Robot Learning (CoRL) 2021

References in corpus (10)

• Group Equivariant Convolutional Networks
• Deep Q-learning from Demonstrations
• Reinforcement Learning with Augmented Data
• Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels
• General $E(2)$-Equivariant Steerable CNNs
• Spatial Action Maps for Mobile Manipulation
• Generalization in Reinforcement Learning by Soft Data Augmentation
• Learning Visual Robotic Control Efficiently with Contrastive Pre-training and Data Augmentation
• Group Equivariant Deep Reinforcement Learning
• Policy learning in SE(3) action spaces