# Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1804.00456 · HTML: https://mlsanity.com/paper/1804.00456/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2018.

## Built on by (most cited first, 14 of 14)

- [Verifying Learning-Based Robotic Navigation Systems](https://mlsanity.com/paper/2205.13536/) (2022)
- [Neuroplastic Expansion in Deep Reinforcement Learning](https://mlsanity.com/paper/2410.07994/) (2024)
- [Curriculum Learning for Safe Mapless Navigation](https://mlsanity.com/paper/2112.12490/) (2021)
- [Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation](https://mlsanity.com/paper/2301.00362/) (2023)
- [Improving Policy Optimization via varepsilon-Retrain](https://mlsanity.com/paper/2406.08315/) (2024)
- [Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics](https://mlsanity.com/paper/2202.11512/) (2022)
- [Policy Augmentation: An Exploration Strategy for Faster Convergence of Deep Reinforcement Learning Algorithms](https://mlsanity.com/paper/2102.05249/) (2021)
- [Image Augmentation Based Momentum Memory Intrinsic Reward for Sparse Reward Visual Scenes](https://mlsanity.com/paper/2205.09448/) (2022)
- [Ensemble Reinforcement Learning in Continuous Spaces -- A Hierarchical Multi-Step Approach for Policy Training](https://mlsanity.com/paper/2209.14488/) (2022)
- [Knowledge is reward: Learning optimal exploration by predictive reward cashing](https://mlsanity.com/paper/2109.08518/) (2021)
- [Deep Reinforcement Learning-Based Mapless Crowd Navigation with Perceived Risk of the Moving Crowd for Mobile Robots](https://mlsanity.com/paper/2304.03593/) (2023)
- [Visual Episodic Memory-based Exploration](https://mlsanity.com/paper/2405.11298/) (2024)
- [Enter the Void - Planning to Seek Entropy When Reward is Scarce](https://mlsanity.com/paper/2505.16787/) (2025)
- [Hierarchical Deep Deterministic Policy Gradient for Autonomous Maze Navigation of Mobile Robots](https://mlsanity.com/paper/2508.04994/) (2025)

## Abstract (arXiv)

This paper investigates exploration strategies of Deep Reinforcement Learning (DRL) methods to learn navigation policies for mobile robots. In particular, we augment the normal external reward for training DRL algorithms with intrinsic reward signals measured by curiosity. We test our approach in a mapless navigation setting, where the autonomous agent is required to navigate without the occupancy map of the environment, to targets whose relative locations can be easily acquired through low-cost solutions (e.g., visible light localization, Wi-Fi signal localization). We validate that the intrinsic motivation is crucial for improving DRL performance in tasks with challenging exploration requirements. Our experimental results show that our proposed method is able to more effectively learn navigation policies, and has better generalization capabilities in previously unseen environments. A video of our experimental results can be found at https://goo.gl/pWbpcF.