Route planning and obstacle avoidance using Q-Learning in MATLAB

Abstract. Recently, autonomous navigation has become important for many applications like self-driving cars, delivery drones, and robots. These domains are typically characterized by issues in planning an efficient route and object avoidance. Therefore, the paper describes work that utilizes Q-Learning as a means of reinforcement learning for autonomous navigation in terrain based on grid representations. Q-Learning allows an agent to acquire optimal signals in terms of strategy without the need for awareness of the surrounding environment. MATLAB was employed for the construction of the Q-Learning algorithm, as it was necessary to execute, evaluate, and simulate the algorithm in a controlled setting. In this study, the agent was tested in other environments of varying sizes (1010, 1515, and 2020) populated with randomly generated obstacles. Benchmark values such as average path length, number of collisions and convergence speed were observed as a measure of the agents performance. The analysis of the values obtained confirms that the agent is able to capture the shortest path from the starting point to the goal while avoiding the obstacles. Q-Learning is found to be flexible and effective in solving the presented navigation problems in this research. The future work would include the enhancement of the efficiency of the algorithm along with the applying of the system in dynamic and more complex scenarios, in order to solve practical problems.