Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning

04/02/2018
by   Oleksii Zhelo, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/19/2022

PIC4rl-gym: a ROS2 modular framework for Robots Autonomous Navigation with Deep Reinforcement Learning

Learning agents can optimize standard autonomous navigation improving fl...
research
09/14/2021

Focus on Impact: Indoor Exploration with Intrinsic Motivation

Exploration of indoor environments has recently experienced a significan...
research
02/07/2018

A Critical Investigation of Deep Reinforcement Learning for Navigation

The navigation problem is classically approached in two steps: an explor...
research
02/22/2022

Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning

Recent years witnessed a remarkable increase in the availability of data...
research
12/29/2022

Visual CPG-RL: Learning Central Pattern Generators for Visually-Guided Quadruped Navigation

In this paper, we present a framework for learning quadruped navigation ...
research
04/09/2020

Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization

Location is key to spatialize internet-of-things (IoT) data. However, it...
research
03/24/2023

Robust Path Following on Rivers Using Bootstrapped Reinforcement Learning

This paper develops a Deep Reinforcement Learning (DRL)-agent for naviga...

Please sign up or login with your details

Forgot password? Click here to reset