# Level Generation¶

Flatland provides multiple ways to create random environments. The most important one is the sparse_rail_generator, which generates realistic-looking railway networks.

## Sparse rail generator¶

The idea behind the sparse rail generator is to mimic classic railway structures where dense nodes (cities) are sparsely connected to each other and where you have to manage traffic flow between the nodes efficiently. The cities in this level generator are much simplified in comparison to real city networks but they mimic parts of the problems faced in daily operations of any railway company.

There are a number of parameters you can tune to build your own map and test different complexity levels of the levels.

Note

Some combinations of parameters do not go well together and will lead to infeasible level generation. In the worst case, the level generator will issue a warning when it cannot build the environment according to the parameters provided.

To build an environment, instantiate a RailEnv as follow:

rail_generator=sparse_rail_generator(
max_num_cities: int = 5,
grid_mode: bool = False,
max_rails_between_cities: int = 4,
max_rails_in_city: int = 4,
seed=0
)

env = RailEnv(
width=50, height=50,
rail_generator=rail_generator,
schedule_generator=sparse_schedule_generator(),
number_of_agents=10
)

env.reset()


You can see that you now need both a rail_generator and a schedule_generator to generate a level. These need to work nicely together. The rail_generator will generate the railway infrastructure and provide hints to the schedule_generator about where to place agents. The schedule_generator will then generate a schedule by placing agents at different train stations and providing them with individual targets.

You can tune the following parameters in the sparse_rail_generator:

• max_num_cities: Maximum number of cities to build. The generator tries to achieve this numbers given all the other parameters. Cities are the only nodes that can host start and end points for agent tasks (train stations).

• grid_mode: How to distribute the cities in the path, either equally in a grid or randomly.

• max_rails_between_cities: Maximum number of rails connecting cities. This is only the number of connection points at city border. The number of tracks drawn in-between cities can still vary.

• max_rails_in_city: Maximum number of parallel tracks inside the city. This represents the number of tracks in the train stations.

• seed: The random seed used to initialize the random generator. Can be used to generate reproducible networks.

## Manually specified railway¶

It is possible to manually design railway networks using rail_from_manual_specifications_generator.

It accepts a list of lists whose each element is a 2-tuple, whose entries represent the cell_type (see core.transitions.RailEnvTransitions) and the desired clockwise rotation of the cell contents (0, 90, 180 or 270 degrees):

specs = [[(0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0)],
[(0, 0), (0, 0), (0, 0), (0, 0), (7, 0), (0, 0)],
[(7, 270), (1, 90), (1, 90), (1, 90), (2, 90), (7, 90)],
[(0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0)]]

env = RailEnv(width=6, height=4,
rail_generator=rail_from_manual_specifications_generator(specs),
number_of_agents=1
env.reset()


## Other rail generators¶

Note

Only the sparse_rail_generator will be used for evaluations in the context of the NeurIPS 2020 challenge.

Other rail generators are available and can be used for example if you want more diversity in your training set: