# One-Shot Reinforcement Learning for Robot Navigation with Interactive Replay

arXiv: https://arxiv.org/abs/1711.10137 · HTML: https://mlsanity.com/paper/1711.10137/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty](https://mlsanity.com/paper/2105.04758/) (2021)
- [Deep Learning for Embodied Vision Navigation: A Survey](https://mlsanity.com/paper/2108.04097/) (2021)
- [Real-Time Reinforcement Learning for Dynamic Tasks with a Parallel Soft Robot](https://mlsanity.com/paper/2509.19525/) (2025)
- [UAS Navigation in the Real World Using Visual Observation](https://mlsanity.com/paper/2208.12125/) (2022)

## Abstract (arXiv)

Recently, model-free reinforcement learning algorithms have been shown to solve challenging problems by learning from extensive interaction with the environment. A significant issue with transferring this success to the robotics domain is that interaction with the real world is costly, but training on limited experience is prone to overfitting. We present a method for learning to navigate, to a fixed goal and in a known environment, on a mobile robot. The robot leverages an interactive world model built from a single traversal of the environment, a pre-trained visual feature encoder, and stochastic environmental augmentation, to demonstrate successful zero-shot transfer under real-world environmental variations without fine-tuning.