Application of Reinforcement Learning in Controlling Quadrotor UAV Flight Actions

Most literature has extensively discussed reinforcement learning (RL) for controlling rotorcraft drones during flight for traversal tasks. However, most studies lack adequate details regarding the design of reward and punishment mechanisms, and there is a limited exploration of the feasibility of applying reinforcement learning in actual flight control following simulation experiments. Consequently, this study focuses on the exploration of reward and punishment design and state input for RL. The simulation environment is constructed using AirSim and Unreal Engine, with onboard camera footage serving as the state input for reinforcement learning. The research investigates three RL algorithms suitable for discrete action training. The Deep Q Network (DQN), Advantage Actor-Critic (A2C), and Proximal Policy Optimization (PPO) were combined with three different reward and punishment design mechanisms for training and testing. The results indicate that employing the PPO algorithm along with a continuous return method as the reward mechanism allows for effective convergence during the training process, achieving a target traversal rate of 71% in the testing environment. Furthermore, this study proposes integrating the YOLOv7-tiny object detection (OD) system to assess the applicability of reinforcement learning in real-world settings. Unifying the state inputs of simulated and OD environments and replacing the original simulated image inputs with a maximum dual-target approach, the experimental simulation achieved a target traversal rate of 52% ultimately. In summary, this research formulates a set of logical frameworks for an RL reward and punishment design deployed with real-time Yolo's OD implementation synergized as a useful aid for related RL studies.