# Towards More Sample Efficiency in Reinforcement Learning with Data Augmentation

arXiv: https://arxiv.org/abs/1910.09959 · HTML: https://mlsanity.com/paper/1910.09959/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning](https://mlsanity.com/paper/2205.00943/) (2022)
- [Deep deterministic policy gradient with symmetric data augmentation for lateral attitude tracking control of a fixed-wing aircraft](https://mlsanity.com/paper/2407.11077/) (2024)

## Abstract (arXiv)

Deep reinforcement learning (DRL) is a promising approach for adaptive robot control, but its current application to robotics is currently hindered by high sample requirements. We propose two novel data augmentation techniques for DRL in order to reuse more efficiently observed data. The first one called Kaleidoscope Experience Replay exploits reflectional symmetries, while the second called Goal-augmented Experience Replay takes advantage of lax goal definitions. Our preliminary experimental results show a large increase in learning speed.