Hybrid Path Planning Algorithm of the Mobile Agent Based on Q-Learning

In the path planning using Q-learning of the mobile agent, the convergence speed is too slow. So, based on Q-learning, two hybrid algorithms are proposed to improve the above problem in this paper. One algorithm is combining Manhattan distance and Q-learning (CMD-QL); the other one is combining flower pollination algorithm and Q-learning (CFPA-QL). In the former algorithm, the Q table is firstly initialized with Manhattan distance to enhance the learning efficiency of the initial stage of Q-learning; secondly, the selection strategy of the epsilon-greedy action is improved to balance the exploration-exploitation relationship of the mobile agent's actions. In the latter algorithm, the flower pollination algorithm is first used to initialize the Q table, so that Q-learning can obtain the necessary prior information which can improve the overall learning efficiency; secondly, the epsilon-greedy strategy under the minimum value of the exploration factor is adopted, which makes effective use of the action with high value. Both algorithms have been tested under known, partially known, and unknown environments, respectively. The test results show that the CMD-QL and CFPA-QL algorithms proposed in this paper can converge to the optimal path faster than the single Q-learning method, besides the CFPA-QL algorithm has the better efficiency.