Deep reinforcement learning.
The system is so efficient because it uses deep reinforcement learning, meaning it actually adapts its processes when it is not doing well and continues improving when it makes progress.
“We have set this up as a traffic control game. The program gets a ‘reward’ when it gets a car through a junction. Every time a car has to wait or there’s a jam, there’s a negative reward. There’s actually no input from us; we simply control the reward system,” said Dr. Maria Chli, a reader in Computer Science at Aston University.
