Autonomous Grading Work Using Deep Reinforcement Learning Based Control

The field of artificial intelligence (AI) has advanced significantly over the years. One of its achievements is the deep reinforcement learning algorithm using which AI can play some Atari 2600 games better than humans. In this paper, optimal route of construction machines such as bulldozers is modeled based on deep reinforcement learning. The aim of this study is to apply deep reinforcement learning to a grading machine to enable it to grade various surface types autonomously. A simple grading simulator is created to simulate the grading task. In addition, the overall scenario is made visible to the network by entering the simulation into the network so that human operators can construct suitable ground path from the surrounding sediment environment. The method is evaluated with the grading simulator, and the agent is shown to exhibit desirable control behavior and fulfill the goals of the simple grading simulation. Despite the environment being virtual, the simulation results demonstrate the feasibility of the proposed approach.