Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (15): 30-36.DOI: 10.3778/j.issn.1002-8331.2001-0347

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3D Path Planning Algorithm Based on Deep Reinforcement Learning

HUANG Dongjin, JIANG Chenfeng, HAN Kaili   

  1. 1.Shanghai Film Academy, Shanghai University, Shanghai 200072, China
    2.Shanghai Engineering Research Center of Motion Picture Special Effects, Shanghai 200072, China
  • Online:2020-08-01 Published:2020-07-30



  1. 1.上海大学 上海电影学院,上海 200072
    2.上海电影特效工程技术研究中心,上海 200072


Reasonable path selection is a difficulty in the field of 3D path planning. The existing 3D path planning methods can not adapt to the unknown terrain, and the obstacle avoidance form is single. In order to solve these problems, a 3D path planning algorithm for agents based on LSTM-PPO is proposed. Virtual ray is designed to detect simulation environment, and the collected state space and action states are introduced into Long Short-Term Memory Networks(LSTM). Through the extra reward function and intrinsic curiosity module, the agent can learn to jump through low obstacles and avoid large obstacles. Using the PPO’s clipped surrogate objective to optimize the update range of planning strategy. The results show that the algorithm is feasible, more intelligent and more reasonable for path planning, and can adapt well to the unknown environment with many obstacles.

Key words: deep reinforcement learning, Proximal Policy Optimization(PPO) algorithm, path planning, complex unknown environment



关键词: 深度强化学习, 近端策略优化算法, 路径规划, 复杂未知场景