Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning

TL;DR

This paper introduces a curiosity-driven intrinsic reward mechanism to enhance deep reinforcement learning for mapless navigation, enabling robots to better explore and generalize in unseen environments without prior maps.

Contribution

The paper proposes a novel intrinsic reward based on curiosity for DRL in mapless navigation, improving exploration and generalization capabilities.

Findings

Intrinsic motivation significantly improves navigation policy learning.

The method outperforms baseline approaches in unseen environments.

Enhanced exploration leads to better generalization in complex settings.

Abstract

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.