AI Learns to Park - Deep Reinforcement Learning

An AI learns to park a car in a parking lot in a 3D physics simulation. The AI consists of a deep Neural Network with 3 hidden layers of 128 neurons each. It is trained with the Proximal Policy Optimization (PPO) algorithm, which is a Reinforcement Learning approach. Basically, the input of the Neural Network are the readings of eight depth sensors, the cars current speed and position, as well as its relative position to the target. The outputs of the Neural Network are interpreted as engine force, braking force and turning force.