Open AccessThe Shortest Path Planning Based on Reinforcement Learning
Author(s) -
Xiaoqi Wang,
Lina Jin,
Haiping Wei
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1584/1/012006
Subject(s) - reinforcement learning , shortest path problem , motion planning , computer science , path (computing) , mathematical optimization , process (computing) , obstacle avoidance , constrained shortest path first , convergence (economics) , pathfinding , q learning , artificial intelligence , k shortest path routing , mathematics , theoretical computer science , mobile robot , robot , graph , economics , programming language , economic growth , operating system
This paper proposes a shortest path planning of agent in an environment based on reinforcement learning. This method adopts the Q-learning algorithm, which has gained increasingly using in agent path planning recently. This algorithm can be fully designed to a reasonable environment model and applied to other professional fields. Yet, reinforcement learning in path planning still needs to improve the rate of convergence. The learning process takes several iterations and spends a long time to find the final path. A local optimal problem in search process deals with other explorations that provided shortest path for agent to take optimal actions. For the agent, the reward value of inclined movement is introduced to find the shortest path. The autonomous obstacle avoidance is utilized to obtain the optimal path in the process. Finally, the model of intelligent agent movement environment has established in matlab. The effectiveness of the algorithm has proved by simulation results.