diff --git a/examples/flatland_2_0_example.py b/examples/flatland_2_0_example.py
index 3bc31626fd017e0e8ad0b33165c3cc90cdd6b8d0..2a6d90b02de79ca40c09bdcd0c80888386b4c8f8 100644
--- a/examples/flatland_2_0_example.py
+++ b/examples/flatland_2_0_example.py
@@ -37,7 +37,7 @@ env = RailEnv(width=50,
max_tracks_in_city=4,
),
schedule_generator=sparse_schedule_generator(),
- number_of_agents=15,
+ number_of_agents=50,
stochastic_data=stochastic_data, # Malfunction data generator
obs_builder_object=GlobalObsForRailEnv())
diff --git a/flatland/envs/rail_generators.py b/flatland/envs/rail_generators.py
index 0bdf5165ac8ccecbd3b6f373f07fc1bef5895442..ac898d439ab4db1b2c83ab7a31fcfa4903e668da 100644
--- a/flatland/envs/rail_generators.py
+++ b/flatland/envs/rail_generators.py
@@ -545,7 +545,7 @@ def sparse_rail_generator(num_cities=5, grid_mode=False, max_inter_city_rails=4,
:param seed: Random seed to initiate rail
:return: generator
"""
- G = nx.Graph()
+ G = nx.DiGraph()
def generator(width, height, num_agents, num_resets=0) -> RailGeneratorProduct:
@@ -605,10 +605,10 @@ def sparse_rail_generator(num_cities=5, grid_mode=False, max_inter_city_rails=4,
print("City build time", time.time() - city_build_time)
# Populate cities
train_station_time = time.time()
- train_stations, track_numbers, built_num_trainstation = _set_trainstation_positions(node_positions,
- city_orientations,
- through_tracks,
- node_radius, grid_map)
+ train_stations, built_num_trainstation = _set_trainstation_positions(node_positions,
+ city_orientations,
+ through_tracks,
+ node_radius, grid_map)
print("Trainstation placing time", time.time() - train_station_time)
# Adjust the number of agents if you could not build enough trainstations
@@ -629,8 +629,7 @@ def sparse_rail_generator(num_cities=5, grid_mode=False, max_inter_city_rails=4,
'num_agents': num_agents,
'agent_start_targets_nodes': agent_start_targets_nodes,
'train_stations': train_stations,
- 'city_orientations': city_orientations,
- 'track_numbers': track_numbers
+ 'city_orientations': city_orientations
}}
def _generate_random_node_positions(nb_nodes, node_radius, height, width):
@@ -763,7 +762,7 @@ def sparse_rail_generator(num_cities=5, grid_mode=False, max_inter_city_rails=4,
tmp_direction = (direction - 1) % 4
while neighb_idx is None:
neighb_idx = neighbours[tmp_direction]
- tmp_direction = (tmp_direction + 1) % 4
+ tmp_direction = (direction + 1) % 4
connected_to_city.append(neighb_idx)
for tmp_out_connection_point in connection_points[current_node][direction]:
@@ -780,7 +779,7 @@ def sparse_rail_generator(num_cities=5, grid_mode=False, max_inter_city_rails=4,
neighb_connection_point = tmp_in_connection_point
new_line = connect_cities(rail_trans, grid_map, tmp_out_connection_point, neighb_connection_point,
city_cells)
- G.add_edge(current_node, neighb_idx)
+ G.add_edge(current_node, neighb_idx, direction=direction, length=len(new_line))
all_paths.extend(new_line)
direction += 1
@@ -847,7 +846,7 @@ def sparse_rail_generator(num_cities=5, grid_mode=False, max_inter_city_rails=4,
"""
nb_nodes = len(node_positions)
train_stations = [[] for i in range(nb_nodes)]
- train_station_orientations = [[] for i in range(nb_nodes)]
+
built_num_trainstations = 0
for current_city in range(len(node_positions)):
for possible_location in _city_cells(node_positions[current_city], node_radius - 1):
@@ -862,9 +861,9 @@ def sparse_rail_generator(num_cities=5, grid_mode=False, max_inter_city_rails=4,
built_num_trainstations += 1
track_nbr = _track_number(node_positions[current_city], city_orientations[current_city],
possible_location)
- train_stations[current_city].append(possible_location)
- train_station_orientations[current_city].append(track_nbr)
- return train_stations, train_station_orientations, built_num_trainstations
+ train_stations[current_city].append((possible_location, track_nbr))
+
+ return train_stations, built_num_trainstations
def _generate_start_target_pairs(num_agents, nb_nodes, train_stations):
"""
@@ -912,8 +911,10 @@ def sparse_rail_generator(num_cities=5, grid_mode=False, max_inter_city_rails=4,
if found_agent_pair:
node_available_start[start_node] -= 1
node_available_target[target_node] -= 1
- agent_start_targets_nodes.append((start_node, target_node))
- print(agent_idx, "has connection", nx.astar_path(G, start_node, target_node))
+ shortest_path = nx.astar_path(G, start_node, target_node, weight='length')
+ start_orientation = nx.get_edge_attributes(G, "direction")[(shortest_path[0], shortest_path[1])]
+ agent_start_targets_nodes.append((start_node, target_node, start_orientation))
+
else:
num_agents -= 1
return agent_start_targets_nodes, num_agents
diff --git a/flatland/envs/schedule_generators.py b/flatland/envs/schedule_generators.py
index 86a41af0ec5ef83ec7d306b25a420d2cac32f31a..cd2f39962a0bb61a6340cacf723731ace686f9e4 100644
--- a/flatland/envs/schedule_generators.py
+++ b/flatland/envs/schedule_generators.py
@@ -63,7 +63,6 @@ def sparse_schedule_generator(speed_ratio_map: Mapping[float, float] = None) ->
agent_start_targets_nodes = hints['agent_start_targets_nodes']
max_num_agents = hints['num_agents']
city_orientations = hints['city_orientations']
- track_numbers = hints['track_numbers']
if num_agents > max_num_agents:
num_agents = max_num_agents
warnings.warn("Too many agents! Changes number of agents.")
@@ -77,43 +76,31 @@ def sparse_schedule_generator(speed_ratio_map: Mapping[float, float] = None) ->
current_target_node = agent_start_targets_nodes[agent_idx][1]
target_station_idx = np.random.randint(len(train_stations[current_target_node]))
target = train_stations[current_target_node][target_station_idx]
- tries = 0
- while (target[0], target[1]) in agents_target:
- target_station_idx = np.random.randint(len(train_stations[current_target_node]))
- target = train_stations[current_target_node][target_station_idx]
- tries += 1
- if tries > 100:
- warnings.warn("Could not set target position, removing an agent")
- break
- agents_target.append((target[0], target[1]))
+ train_stations[current_target_node].pop(target_station_idx)
+ agents_target.append((target[0][0], target[0][1]))
- # Set start for agent
+ # Set start for agent and corresponding orientation
current_start_node = agent_start_targets_nodes[agent_idx][0]
- start_station_idx = np.random.randint(len(train_stations[current_start_node]))
- start = train_stations[current_start_node][start_station_idx]
- current_track_nbr = track_numbers[current_start_node][start_station_idx]
- tries = 0
- while (start[0], start[1]) in agents_position:
- tries += 1
- if tries > 100:
- warnings.warn("Could not set start position, please change initial parameters!!!!")
+ agent_start_orientation = agent_start_targets_nodes[agent_idx][2]
+
+ # Place the agent on the corresponding track
+ if city_orientations[current_start_node] == agent_start_orientation:
+ track_to_use = 0
+ else:
+ track_to_use = 1
+
+ for i in range(len(train_stations[current_start_node])):
+ if train_stations[current_start_node][i][1] == track_to_use:
+ start_station_idx = i
break
- start_station_idx = np.random.randint(len(train_stations[current_start_node]))
- start = train_stations[current_start_node][start_station_idx]
- current_track_nbr = track_numbers[current_start_node][start_station_idx]
- agents_position.append((start[0], start[1]))
+ start = train_stations[current_start_node][start_station_idx]
+ train_stations[current_start_node].pop(start_station_idx)
+ agents_position.append((start[0][0], start[0][1]))
+ agents_direction.append(agent_start_orientation)
# Orient the agent correctly
- if current_track_nbr % 2 != 0:
- current_orientation = city_orientations[current_start_node]
- agents_direction.append(current_orientation)
- else:
- current_orientation = (city_orientations[current_start_node] + 2) % 2
- agents_direction.append(current_orientation)
- if not rail.check_path_exists(start, current_orientation, target):
- print("No path")
if speed_ratio_map:
speeds = speed_initialization_helper(num_agents, speed_ratio_map)