@@ -25,12 +25,15 @@ It can be used for many learning task where a two-dimensional grid could be the
Flatland delivers a python implementation which can be easily extended. And it provides different baselines for different environments.
Each environment enables an interesting task to solve. For example, the mutli-agent navigation task for railway train dispatching is a very exciting topic.
It can be easily extended or adapted to the airplane landing problem. This can further be the basic implementation for many other task in transporation and logcistics.
The railway environment has a very restricted transition behaviour. Trains can normally not run backwards and the have to follow rails.
The can only switch cells along rails or the pass a switch in right direction. Thus the navigation behaviour of a train is very restricted.
The planning problem where many agents share same infrastructure becomes mostly to an ordering problem.
Trains have a departing location and a destination where they have to travel to. The agents have to learn to avoid each others or to learn to pass.
Otherwise the can never successfully reach the destinations. In complex situation they have to learn to cooperate otherwise they get stocked in dead-lock situation.
This make the railway planning problem a very complex mulit-agent reinforcement task.
Mapping a railway infrastructure into a grid world is an excellent example showing how the movement can of an agent can be easily restricted with the help of the cell's transition maps.
As trains can normally not run backwards and they have to follow rails the transition for one cell ot the other depends also the train's orientation.
Trains can only change the traveling path at switches. There a two variants of switches. The first kind of switch is the splitting "switch", where trains can change rails and in consequence the traveling path.
The second kind of swtich is the fusion switch, where train can change order. That means two rails come together. Thus the navigation behaviour of a train is very restricted.
The railway planning problem where many agents share same infrastructure is a very complex problem. If trains can not change traveling path, the underlaying problem will be an ordering problem. Even the ordering
problem is very hard to solve.
Furthermore trains have a departing location where they can not depart earlier than a committed time. Then the have to arrive at destination not later than the second committed time. This makes the whole planning problem
still more complicated. In such a complex environment cooperation is essential. Thus agents have to learn to cooperate in a way that all trains (agents) arrive on time.
