Reinforcement Learning for Solving Control Problems in Robotics

The use of reinforcement learning technology for the optimal control problem solution is considered. To solve the optimal control problem an evolutionary algorithm is used that finds control to ensure the movements of a control object along different trajectories with approximately the same values of the quality criterion. Additional conditions for passing the trajectory in the neighbourhood of given areas of the state space are included in the quality criterion. To build a stabilization system for the movement of an object along a given trajectory, machine learning control by symbolic regression is used. An example of solving the optimal control problem for a quadcopter is given.