Research on path planning techniques based on deep reinforcement learning

Abstract

Path planning problem stands for a sort of problem that find a secure path from the departure position to the destination in the unique application area without conflicts with other objects under the premise of minimum time or distance cost. Path planning has many applications in real life. This paper summarizes and classifies the current main research results on path planning, which are classified according to conventional techniques, single-agent path planning techniques and multi-agent path planning techniques. Conventional techniques are classified into three different algorithms: Traditional techniques, Graphics techniques and Intelligent bionics techniques. For the single-agent path planning techniques, it is primarily broken down into two categories: value-based and strategy-based. For multi-agent techniques, according to the improvement techniques, it is split in three categories: expert demonstration type, improved communication type and task decomposition type. Based on above classification, the mechanisms, advantages, and disadvantages of the existing algorithms are summarized and compared, and the future work in this field is prospected.