Path Planning for Outdoor Mobile Robots Based on IDDQN
Jiang Shuhai,
Sun Shangjie,
Li Cun
Abstract:Path planning is one of the research hotspots for outdoor mobile robots. This paper addresses the issues of slow convergence and low accuracy in the Double Deep Q Network (DDQN) method in environments with many obstacles in the context of deep reinforcement learning. A new algorithm, Improve Double Deep Q Network (IDDQN), is proposed, which utilizes second-order temporal difference methods and a binary tree data structure to improve the DDQN method. The improved method evaluates the actions of the current robo… Show more
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