Obstacle Avoidance and Navigation Utilizing Reinforcement Learning with Reward Shaping
University of North Texas（北德克萨斯大学）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
