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Obstacle Avoidance and Navigation Utilizing Reinforcement Learning with Reward Shaping
March 28, 2020 Β· Declared Dead Β· π Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II
"No code URL or promise found in abstract"
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Authors
Daniel Zhang, Colleen P. Bailey
arXiv ID
2003.12863
Category
cs.RO: Robotics
Cross-listed
cs.LG,
eess.SY
Citations
16
Venue
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II
Last Checked
4 months ago
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.
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