Deep Deterministic Policy Gradient-Based Autonomous Driving for Mobile Robots in Sparse Reward Environments

In this paper, we propose a deep deterministic policy gradient (DDPG)-based path-planning method for mobile robots by applying the hindsight experience replay (HER) technique to overcome the performance degradation resulting from sparse reward problems occurring in autonomous driving mobile robots....

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Bibliographic Details

Published in:	Sensors
Main Authors:	Minjae Park, Seok Young Lee, Jin Seok Hong, Nam Kyu Kwon
Format:	Article
Language:	English
Published:	MDPI AG 2022-12-01
Subjects:	reinforcement learning deep deterministic policy gradient hindsight experience replay mobile robot autonomous driving sparse reward environments
Online Access:	https://www.mdpi.com/1424-8220/22/24/9574