diff --git a/docs/flatland_2.0.md b/docs/flatland_2.0.md
index 0ed2f1bdcd2e460fb60b1982047d03dc223509e2..03c1ff391e9b2193fca6390a3dadeb0aa196129d 100644
--- a/docs/flatland_2.0.md
+++ b/docs/flatland_2.0.md
@@ -43,7 +43,7 @@ RailGenerator = sparse_rail_generator(num_cities=10, # Nu
node_radius=3, # Proximity of stations to city center
num_neighb=3, # Number of connections to other cities
seed=5, # Random seed
- realistic_mode=True # Ordered distribution of nodes
+ grid_mode=True # Ordered distribution of nodes
)
# Build the environment
@@ -57,22 +57,22 @@ env = RailEnv(width=50,
You can tune the following parameters:
-- `num_citeis` is the number of cities on a map. Cities are the only nodes that can host start and end points for agent tasks (Train stations). Here you have to be carefull that the number is not too high as all the cities have to fit on the map. When `realistic_mode=False` you have to be carefull when chosing `min_node_dist` because leves will fails if not all cities (and intersections) can be placed with at least `min_node_dist` between them.
+- `num_citeis` is the number of cities on a map. Cities are the only nodes that can host start and end points for agent tasks (Train stations). Here you have to be carefull that the number is not too high as all the cities have to fit on the map. When `grid_mode=False` you have to be carefull when chosing `min_node_dist` because leves will fails if not all cities (and intersections) can be placed with at least `min_node_dist` between them.
- `num_intersections` is the number of nodes that don't hold any trainstations. They are also the first priority that a city connects to. We use these to allow for sparse connections between cities.
- `num_trainstations`defines the *Total* number of trainstations in the network. This also sets the max number of allowed agents in the environment. This is also a delicate parameter as there is only a limitid amount of space available around nodes and thus if the number is too high the level generation will fail. *Important*: Only the number of agents provided to the environment will actually produce active train stations. The others will just be present as dead-ends (See figures below).
-- `min_node_dist`is only used if `realistic_mode=False` and represents the minimal distance between two nodes.
+- `min_node_dist`is only used if `grid_mode=False` and represents the minimal distance between two nodes.
- `node_radius` defines the extent of a city. Each trainstation is placed at a distance to the closes city node that is smaller or equal to this number.
- `num_neighb`defines the number of neighbouring nodes that connect to each other. Thus this changes the connectivity and thus the amount of alternative routes in the network.
- `seed` is used to initialize the random generator
-- `realistic_mode` currently only changes how the nodes are distirbuted. If it is set to `True` the nodes are evenly spreas out and cities and intersecitons are set between each other.
+- `grid_mode` currently only changes how the nodes are distirbuted. If it is set to `True` the nodes are evenly spreas out and cities and intersecitons are set between each other.
If you run into any bugs with sets of parameters please let us know.
-Here is a network with `realistic_mode=False` and the parameters from above.
+Here is a network with `grid_mode=False` and the parameters from above.
-and here with `realistic_mode=True`
+and here with `grid_mode=True`
diff --git a/examples/flatland_2_0_example.py b/examples/flatland_2_0_example.py
index 1a18cb407942e66b5a057f7a2b3ad0f61a730a22..f8a613779c2afcb7955184b80c2098bf76e64a38 100644
--- a/examples/flatland_2_0_example.py
+++ b/examples/flatland_2_0_example.py
@@ -34,16 +34,16 @@ env = RailEnv(width=50,
height=50,
rail_generator=sparse_rail_generator(num_cities=25, # Number of cities in map (where train stations are)
num_intersections=0, # Number of intersections (no start / target)
- num_trainstations=0, # Number of possible start/targets on map
+ num_trainstations=50, # Number of possible start/targets on map
min_node_dist=3, # Minimal distance of nodes
node_radius=2, # Proximity of stations to city center
num_neighb=3, # Number of connections to other cities/intersections
seed=15, # Random seed
- realistic_mode=True,
+ grid_mode=True,
enhance_intersection=False
),
schedule_generator=sparse_schedule_generator(speed_ration_map),
- number_of_agents=0,
+ number_of_agents=20,
stochastic_data=stochastic_data, # Malfunction data generator
obs_builder_object=TreeObservation)
diff --git a/flatland/envs/rail_generators.py b/flatland/envs/rail_generators.py
index 93e7ce55ebd590f6a81cb389a0a3ec685e800ae3..39796515f73c7702ba4dc162301a63b0186dc1d3 100644
--- a/flatland/envs/rail_generators.py
+++ b/flatland/envs/rail_generators.py
@@ -527,7 +527,7 @@ def random_rail_generator(cell_type_relative_proportion=[1.0] * 11) -> RailGener
def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2, min_node_dist=20, node_radius=2,
- num_neighb=3, realistic_mode=False, enhance_intersection=False, seed=0):
+ num_neighb=3, grid_mode=False, enhance_intersection=False, seed=0):
"""
This is a level generator which generates complex sparse rail configurations
@@ -537,7 +537,7 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
:param min_node_dist: Minimal distance between nodes
:param node_radius: Proximity of trainstations to center of city node
:param num_neighb: Number of neighbouring nodes each node connects to
- :param realistic_mode: True -> NOdes evenly distirbuted in env, False-> Random distribution of nodes
+ :param grid_mode: True -> NOdes evenly distirbuted in env, False-> Random distribution of nodes
:param enhance_intersection: True -> Extra rail elements added at intersections
:param seed: Random Seed
:return:
@@ -565,7 +565,7 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
intersection_positions = []
# Evenly distribute cities and intersections
- if realistic_mode:
+ if grid_mode:
tot_num_node = num_intersections + num_cities
nodes_ratio = height / width
nodes_per_row = int(np.ceil(np.sqrt(tot_num_node * nodes_ratio)))
@@ -581,7 +581,7 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
to_close = True
tries = 0
- if not realistic_mode:
+ if not grid_mode:
while to_close:
x_tmp = node_radius + np.random.randint(height - node_radius)
y_tmp = node_radius + np.random.randint(width - node_radius)
diff --git a/tests/test_flatland_envs_sparse_rail_generator.py b/tests/test_flatland_envs_sparse_rail_generator.py
index db7cac61f4cf3bec4a330694c1864ef7d82bd076..c60d50622f630a79cd0326518cf99f1714062d27 100644
--- a/tests/test_flatland_envs_sparse_rail_generator.py
+++ b/tests/test_flatland_envs_sparse_rail_generator.py
@@ -15,7 +15,7 @@ def test_sparse_rail_generator():
node_radius=3, # Proximity of stations to city center
num_neighb=3, # Number of connections to other cities
seed=5, # Random seed
- realistic_mode=False # Ordered distribution of nodes
+ grid_mode=False # Ordered distribution of nodes
),
schedule_generator=sparse_schedule_generator(),
number_of_agents=10,