CyberRunner is an AI robot whose task is to learn how to play the popular and widely accessible labyrinth marble game. The labyrinth is a game of physical skill whose goal is to steer a marble from a given start point to the end point. In doing so, the player must prevent the ball from falling into any of the holes that are present on the labyrinth board.

The movement of the ball can be indirectly controlled by two knobs which change the orientation of the board. While it is a relatively straightforward game, it requires fine motor skills and spatial reasoning abilities, and, from experience, humans require a great amount of practice to become proficient at the game.

CyberRunner exploits recent advances in model-based reinforcement learning and its ability to make informed decisions about potentially successful behaviors by planning into the future. The robot learns by collecting experience. While playing the game, it captures observations and receives rewards based on its performance, all through the “eyes” of a camera looking down at the labyrinth. A memory is kept of the collected experience. Using this memory, the model-based reinforcement learning algorithm learns how the system behaves, and based on its understanding of the game it recognizes which strategies and behaviors are more promising. Consequently, the way the robot uses the two motors – its “hands” – to play the game is continuously improved. Importantly, the robot does not stop playing to learn; the algorithm runs concurrently with the robot playing the game. As a result, the robot keeps getting better, run after run.