minor bugfixes

examples/flatland_2_0_example.py

@@ -30,18 +30,18 @@ speed_ration_map = {1.: 0.25,  # Fast passenger train
 
 env = RailEnv(width=50,
               height=50,
-              rail_generator=sparse_rail_generator(num_cities=30,  # Number of cities in map (where train stations are)
+              rail_generator=sparse_rail_generator(num_cities=20,  # Number of cities in map (where train stations are)
                                                    num_intersections=5,  # Number of intersections (no start / target)
-                                                   num_trainstations=20,  # Number of possible start/targets on map
+                                                   num_trainstations=15,  # 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
+                                                   num_neighb=4,  # Number of connections to other cities/intersections
                                                    seed=15,  # Random seed
                                                    realistic_mode=True,
                                                    enhance_intersection=True
                                                    ),
               schedule_generator=sparse_schedule_generator(speed_ration_map),
-              number_of_agents=40,
+              number_of_agents=10,
               stochastic_data=stochastic_data,  # Malfunction data generator
               obs_builder_object=TreeObservation)
 

flatland/envs/rail_generators.py

@@ -573,14 +573,15 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
             x_positions = np.linspace(node_radius, height - node_radius, nodes_per_row, dtype=int)
             y_positions = np.linspace(node_radius, width - node_radius, nodes_per_col, dtype=int)
 
-        fraction = 0
-        city_fraction = num_cities / tot_num_node
-        step = np.gcd(num_intersections, num_cities) / tot_num_node
+            fraction = 0
+            city_fraction = num_cities / tot_num_node
+            step = np.gcd(num_intersections, num_cities) / tot_num_node
+
         for node_idx in range(num_cities + num_intersections):
             to_close = True
             tries = 0
 
-            fraction = (fraction + step) % 1.
+
             if not realistic_mode:
                 while to_close:
                     x_tmp = node_radius + np.random.randint(height - node_radius)
@@ -608,13 +609,13 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
                         warnings.warn("Could not set nodes, please change initial parameters!!!!")
                         break
             else:
+                fraction = (fraction + step) % 1.
                 x_tmp = x_positions[node_idx % nodes_per_row]
                 y_tmp = y_positions[node_idx // nodes_per_row]
                 if len(city_positions) < num_cities and fraction < city_fraction:
                     city_positions.append((x_tmp, y_tmp))
                 else:
                     intersection_positions.append((x_tmp, y_tmp))
-        print(len(city_positions))
         node_positions = city_positions + intersection_positions
 
         # Chose node connection
@@ -634,13 +635,13 @@ def sparse_rail_generator(num_cities=5, num_intersections=4, num_trainstations=2
             available_nodes_full = np.delete(available_nodes_full, delete_idx, 0)
 
             # Priority city to intersection connections
-            if current_node < num_cities and len(available_intersections) > 0:
+            if False and current_node < num_cities and len(available_intersections) > 0:
                 available_nodes = available_intersections
                 delete_idx = np.where(available_cities == current_node)
                 available_cities = np.delete(available_cities, delete_idx, 0)
 
             # Priority intersection to city connections
-            elif current_node >= num_cities and len(available_cities) > 0:
+            elif False and current_node >= num_cities and len(available_cities) > 0:
                 available_nodes = available_cities
                 delete_idx = np.where(available_intersections == current_node)
                 available_intersections = np.delete(available_intersections, delete_idx, 0)