A Multi-Dimensional Goal Aircraft Guidance Approach Based on Reinforcement Learning with a Reward Shaping Algorithm

Guiding an aircraft to 4D waypoints at a certain heading is a multi-dimensional goal aircraft guidance problem. [d=Zu]In order to improve the performance and solve this problem, this paper proposes a multi-layer RL approach.To enhance the performance, in the present study, a multi-layer RL approach to solve the multi-dimensional goal aircraft guidance problem is proposed. The approach [d=Zu]enablesassists the autopilot in an ATC simulator to guide an aircraft to 4D waypoints at certain latitude, longitude, altitude, heading, and arrival time, respectively. To be specific, a multi-layer RL [d=Zu]approach is proposedmethod to simplify the neural network structure and reduce the state dimensions. A shaped reward function that involves the potential function and Dubins path method is applied. [d=Zu]Experimental and simulation results show that the proposed approachExperiments are conducted and the simulation results reveal that the proposed method can significantly improve the convergence efficiency and trajectory performance. [d=Zu]FurthermoreFurther, the results indicate possible application prospects in team aircraft guidance tasks, since the aircraft can directly approach a goal without waiting in a specific pattern, thereby overcoming the problem of current ATC simulators.

How to build a new athletic track to break records

We introduce a new optimal control model which encompasses pace optimization and motor control effort for a runner on a fixed distance. The system couples mechanics, energetics, neural drive to an economic decision theory of cost and benefit. We find how effort is minimized to produce the best running strategy, in particular, in the bend. This allows us to discriminate between different types of tracks and estimate the discrepancy between lanes. Relating this model to the optimal path problem called the Dubins path, we are able to determine the geometry of the optimal track and estimate record times.