Obstacle Avoidance and Navigation Utilizing Reinforcement Learning with Reward Shaping

2020/03/28 by Daniel Zhang, Zhang, Daniel, Colleen P. Bailey +1 · 4 citations
Computer Science · Engineering · #Artificial intelligence #Autonomous Vehicle Technology and Safety #Collision avoidance #Computer science #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Mobile robot #Obstacle #Obstacle avoidance #Reinforcement Learning in Robotics #Reinforcement learning #Robot #Robotic Path Planning Algorithms #Robotics (cs.RO) #Systems and Control (eess.SY) #cs.LG #cs.RO #cs.SY #eess.SY #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2003.12863

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2020/03/28 · openalex created_date 2020/04/03 · arxiv created 2020/04/10 · arxiv updated 2020/04/13 · openalex updated_date 2026/07/28

Abstract

In this paper, we investigate the obstacle avoidance and navigation problem in the robotic control area. For solving such a problem, we propose revised Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization algorithms with an improved reward shaping technique. We compare the performances between the original DDPG and PPO with the revised version of both on simulations with a real mobile robot and demonstrate that the proposed algorithms achieve better results.

Citations

• Adam: A Method for Stochastic Optimization
• Playing Atari with Deep Reinforcement Learning
• Trust Region Policy Optimization
• Continuous control with deep reinforcement learning
• Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
• Benchmarking Reinforcement Learning Algorithms on Real-World Robots

Cited by

• Deep Reinforcement Learning with anticipatory reward in LSTM for Collision Avoidance of Mobile Robots

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• Generalization in Deep Reinforcement Learning for Robotic Navigation by Reward Shaping
• Arena-Rosnav: Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems