Research on the shortest path problem of single source based on Q-learning

In the field of algorithm research, the problem of single-source shortest path has been discussed for a long time. In this paper, the problem of single source shortest path based on Q-learning is studied. Q-learning is a reinforcement learning algorithm that iterates to update the Q value of each state-action pair to determine the optimal path. When solving the single-source shortest path problem, each node in the graph is regarded as a state, and the edge between each node as an action. By using the Q-learning algorithm, we can find the optimal strategy, that is, to minimize the total cost from the starting point to the end point, and then find the shortest path. This paper introduces the implementation process of Q-learning algorithm, including initializing Q-table, selecting action, executing action, updating Q-table and so on. Finally, the effectiveness and feasibility of Q-learning algorithm are verified by experiments.