Obstacle avoidance and navigation utilizing reinforcement learning with reward shaping

Daniel Zhang; Colleen P. Bailey

doi:10.1117/12.2558212

21 April 2020 Obstacle avoidance and navigation utilizing reinforcement learning with reward shaping

Daniel Zhang, Colleen P. Bailey

Proceedings Volume 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II; 114131H (2020) https://doi.org/10.1117/12.2558212
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

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 performance 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.

Conference Presentation

Citation Download Citation

Daniel Zhang and Colleen P. Bailey "Obstacle avoidance and navigation utilizing reinforcement learning with reward shaping", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114131H (21 April 2020); https://doi.org/10.1117/12.2558212

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