关键词: 路径规划, 强化学习, 人工势场, 陷阱搜索, Q值初始化

Abstract: It is difficult to obtain a better path for mobile robot in complex environment. The Q-learning algorithm based on the Markov process can achieve better path through learning by trial and error. But this algorithm has a slow convergence speed and a large number of iterations, the trial and error approach cannot be applied in the real environment. Search trap area on the basis of adding gravitational potential field as the initial environment priori information in the Q-learning algorithm, remove [Q] value iteration in concave trap area which speeding up the convergence rate of path planning. At the same time, cancel the trial and error learning to the obstacle, the algorithm avoids obstacles effectively in the initial state. It can be applied in the real environment. Python and pygame modules are used to build complex maps to verify the path planning effect of the improved Q-learning algorithm with the addition of initial gravitational potential field and trap search. The simulation results show that the improved algorithm can reach the target position quickly and effectively after fewer iterations.

Key words: path planning, reinforcement learning, artificial potential field, trap search, Q value initialization