This project demonstrates how machine learning algorithm can be used to teach a rocket to take off from one planet, then fly and land on another one.
The following video demonstrates the final result (builds are attached at the end of this article):
Game area is a rectangular area that contains two planets - Planet 1 and Planet 2.
Planet 1 is randomly positioned in a small central part of the game area, Planet 2 is placed randomly in the remaining space.
Planets have gravity fields, they are denoted by circles around the planets.
At the beginning of each session rocket is located on Planet 1.
Rocket has six engines that apply different accelerations (they are denoted by red arrows in the picture below) at different positions.
Success \ Goal
The goal is to take off the rocket from Planet 1 and to land it on Planet 2. Landing is considered successful when both landing legs touch Planet 2.
The following situations are considered as failures:
Rocket hits any of the planets with its hull.
Rocket moves out of the game area.
Rocket starts flying too fast (faster then some predefined constant). This condition is applied to ML-Agent simulation, when rocket is controlled manually by a player this condition is removed.
Rocket starts rotating to fast (faster then some predefined constant). This condition is also applied only to ML-Agent simulation, when rocket is controlled manually by a player this condition is also removed.
ML-Agent's brain uses discrete action space type - 6 actions are used for different engines, and one action is a "do nothing" action.
The training was performed in a few separate steps:
The model was trained on a medium size field without curriculum learning until it reached a good enough success rate.
Then the training was continued using curriculum learning - on each step the size of the game area was increased until all curriculum learning lessons were completed.
After that the training was continued with modified Hyperparameters ("beta" was reduced).
After these training sessions were conducted, the algorithm was able to achieve ~91-93% success rate on a large game field.
Simulation can be switched to manual control mode by pressing "Brain: ML-Agent" button at the top of the screen. In the manual mode use the following keys to control the rocket:
"Q" - upper left side engine;
"E" - upper right side engine;
"A" - bottom left side engine;
"D" - bottom right side engine;
"S" - main bottom engine;
"W" - top stop engine.
The training was performed on an old mobile Geforce GTX 460M graphics card and it was rather slow. It is obvious that the above-described training process can be improved in many ways.
Unity Machine Learning tools open wide opportunities for game developers.