Design of Agent Training Environment for Aircraft Landing Guidance Based on Deep Reinforcement Learning

Recent advances in deep reinforcement learning have shown promising potential results in solving complex control problems. Inspired by these achievements, in this paper, an environment which can be applied in agent training to solve aircraft landing guidance problem is proposed. By this environment, the agent receives the current state of the aircraft and the runway in a sector and outputs an action to guide the aircraft to land at the runway. The Deep Q Network (DQN) algorithm is used to verify the feasibility of the environment. The experimental results show that this environment can be used to train an agent, and the guidance action produced by the agent is consistent with the behavior of air traffic controllers.