Multi Agent Reinforcement Learning in Flatland Domain

Coordinating the movement of multiple trains is a hard planning task. The computational complexity of solving such a problem is exponential in the number of trains in the general case. The project deals with solving the Flatland domain, which simulates the train coordination problem. It is done by arranging the train by priority, and planning the optimal route for each train, given the routes for the other trains. If a train gets off route, its route is planned again while compromising the other routes.