# Augmenting learning using symmetry in a biologically-inspired domain

Source: [Augmenting learning using symmetry in a biologically-inspired domain](<https://www.sota2.com/research/paper/68fa66741e209131cea102d1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:49:28.917Z

Authors: Shruti Mishra, Abbas Abdolmaleki, Arthur Guez, Piotr Trochim, Doina Precup

Published: 2019-10-01T16:29:14.000Z

## Abstract

Invariances to translation, rotation and other spatial transformations are a hallmark of the laws of motion, and have widespread use in the natural sciences to reduce the dimensionality of systems of equations. In supervised learning, such as in image classification tasks, rotation, translation and scale invariances are used to augment training datasets. In this work, we use data augmentation in a similar way, exploiting symmetry in the quadruped domain of the DeepMind control suite (Tassa et al. 2018) to add to the trajectories experienced by the actor in the actor-critic algorithm of Abdolmaleki et al. (2018). In a data-limited regime, the agent using a set of experiences augmented through symmetry is able to learn faster. Our approach can be used to inject knowledge of invariances in the domain and task to augment learning in robots, and more generally, to speed up learning in realistic robotics applications.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1910.00528>)

[PDF](<https://arxiv.org/pdf/1910.00528.pdf>)