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Commit a9716d8a authored by Erik Nygren's avatar Erik Nygren 🚅
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added realistic_mode for less random levels 

added more complex intersections
parent f924d9e2
Pipeline #1767 failed with stages
in 60 minutes
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flatland/core/transition_map.py
View file @ a9716d8a
...@@ -412,6 +412,8 @@ class GridTransitionMap(TransitionMap): ...@@ -412,6 +412,8 @@ class GridTransitionMap(TransitionMap):
grcMax = self.grid.shape grcMax = self.grid.shape
# loop over available outbound directions (indices) for rcPos # loop over available outbound directions (indices) for rcPos
self.set_transitions(rcPos, 0)
incomping_connections = np.zeros(4) incomping_connections = np.zeros(4)
for iDirOut in np.arange(4): for iDirOut in np.arange(4):
gdRC = gDir2dRC[iDirOut] # row,col increment gdRC = gDir2dRC[iDirOut] # row,col increment
......
flatland/envs/generators.py
View file @ a9716d8a
...@@ -543,7 +543,7 @@ def random_rail_generator(cell_type_relative_proportion=[1.0] * 11): ...@@ -543,7 +543,7 @@ def random_rail_generator(cell_type_relative_proportion=[1.0] * 11):
return generator return generator
def realistic_rail_generator(nr_start_goal=1, seed=0,max_add_dead_end = 7): def realistic_rail_generator(nr_start_goal=1, seed=0):
""" """
Parameters Parameters
------- -------
...@@ -746,80 +746,33 @@ def realistic_rail_generator(nr_start_goal=1, seed=0,max_add_dead_end = 7): ...@@ -746,80 +746,33 @@ def realistic_rail_generator(nr_start_goal=1, seed=0,max_add_dead_end = 7):
data = [] data = []
for x_loop in range(int(len(x) / 2)): for x_loop in range(int(len(x) / 2)):
start = ( start = (
max(0, min(off_set + nbr_track_loop + 1, height - 1)), max(0, min(off_set + nbr_track_loop + 1, height - 1)), max(0, min(x[2 * x_loop], width - 1)))
max(0, min(x[2 * x_loop], width - 1)))
goal = ( goal = (
max(0, min(off_set + nbr_track_loop + 1, height - 1)), max(0, min(off_set + nbr_track_loop + 1, height - 1)),
max(0, min(x[2 * x_loop + 1], width - 1))) max(0, min(x[2 * x_loop + 1], width - 1)))
d = np.arange(x[2 * x_loop] + 1, x[2 * x_loop + 1] - 1, 2)
if (off_set + nbr_track_loop + 1 == start[0]) and (off_set + nbr_track_loop + 1 == goal[0]): data.extend(d)
d = np.arange(x[2 * x_loop] + 1, x[2 * x_loop + 1] - 1, 2)
data.extend(d) new_path = connect_rail(rail_trans, rail_array, start, goal)
if len(new_path) > 0:
new_path = connect_rail(rail_trans, rail_array, start, goal) c = (off_set + nbr_track_loop, x[2 * x_loop] + 1)
if len(new_path) > 0: make_switch_e_w(width, height, grid_map, c)
c = (off_set + nbr_track_loop, x[2 * x_loop] + 1) c = (off_set + nbr_track_loop, x[2 * x_loop + 1] + 1)
make_switch_e_w(width, height, grid_map, c) make_switch_w_e(width, height, grid_map, c)
c = (off_set + nbr_track_loop, x[2 * x_loop + 1] + 1)
make_switch_w_e(width, height, grid_map, c) add_pos = (int((start[0] + goal[0]) / 2), int((start[1] + goal[1]) / 2))
if nbr_track_loop % 2 == 0:
add_pos = (int((start[0] + goal[0]) / 2), int((start[1] + goal[1]) / 2)) agents_positions_forward.append(add_pos)
if off_set_loop % 2 == 0: agents_directions_forward.append(([1, 3][off_set_loop % 2]))
agents_positions_forward.append(add_pos) idx_forward.append(idx_target)
agents_directions_forward.append(([1, 3][nbr_track_loop % 2]))
idx_forward.append(idx_target)
else:
agents_positions_backward.append(add_pos)
agents_directions_backward.append(([3, 1][nbr_track_loop % 2]))
idx_backward.append(idx_target)
add_pos = (int((start[0] + goal[0]) / 2), int((2 * start[1] + goal[1]) / 3), idx_target)
agents_targets.append(add_pos)
idx_target += 1
# add dead-end
if True:
n = max_add_dead_end#int(np.random.choice(np.arange(max_add_dead_end-2)+1, 1)[0])
for pos_y in np.random.choice(np.arange(width - 7) + 3, n):
pos_x = off_set
pos_x1 = max(0, min(pos_x + 1, height - 1))
if np.random.random() > 0.5:
if pos_x + 1 < height - 1:
start_track = (pos_x1, pos_y)
goal_track = (pos_x1, pos_y + 1)
ok = True
for k in range(4):
ok &= grid_map.grid[pos_x1][pos_y + (k-1) ] == 0
if ok:
new_path = connect_rail(rail_trans, rail_array, start_track, goal_track)
if len(new_path) > 0:
c = (pos_x1 - 1, pos_y + 1)
make_switch_e_w(width, height, grid_map, c)
add_pos = goal_track # (int((start_track[0] + goal_track[0]) / 2), int((start_track[1] + goal_track[1]) / 2))
agents_positions_forward.append(add_pos)
agents_directions_forward.append(3)
idx_forward.append(idx_target)
agents_targets.append((goal_track[0], goal_track[1], idx_target))
idx_target += 1
else: else:
pos_x = max(0, min(pos_x + 1, height - 1)) agents_positions_backward.append(add_pos)
if pos_x + 1 < height - 1: agents_directions_backward.append(([1, 3][off_set_loop % 2]))
start_track = (pos_x1, pos_y - 1) idx_backward.append(idx_target)
goal_track = (pos_x1, pos_y - 2)
ok = True add_pos = (int((start[0] + goal[0]) / 2), int((2 * start[1] + goal[1]) / 3), idx_target)
for k in range(4): agents_targets.append(add_pos)
ok &= grid_map.grid[pos_x1][pos_y - k] == 0 idx_target += 1
if ok:
new_path = connect_rail(rail_trans, rail_array, start_track, goal_track)
if len(new_path) > 0:
c = (pos_x1 - 1, pos_y)
make_switch_w_e(width, height, grid_map, c)
add_pos = goal_track # (int((start_track[0] + goal_track[0]) / 2), int((start_track[1] + goal_track[1]) / 2))
agents_positions_backward.append(add_pos)
agents_directions_backward.append(1)
idx_backward.append(idx_target)
agents_targets.append((goal_track[0], goal_track[1], idx_target))
idx_target += 1
agents_position = [] agents_position = []
agents_target = [] agents_target = []
...@@ -860,7 +813,7 @@ def realistic_rail_generator(nr_start_goal=1, seed=0,max_add_dead_end = 7): ...@@ -860,7 +813,7 @@ def realistic_rail_generator(nr_start_goal=1, seed=0,max_add_dead_end = 7):
def sparse_rail_generator(num_cities=100, num_intersections=10, num_trainstations=2, min_node_dist=20, node_radius=2, def sparse_rail_generator(num_cities=100, num_intersections=10, num_trainstations=2, min_node_dist=20, node_radius=2,
num_neighb=4, realistic_mode=False, seed=0): num_neighb=4, realistic_mode=False, enhance_intersection=False, seed=0):
''' '''
:param nr_train_stations: :param nr_train_stations:
...@@ -897,6 +850,7 @@ def sparse_rail_generator(num_cities=100, num_intersections=10, num_trainstation ...@@ -897,6 +850,7 @@ def sparse_rail_generator(num_cities=100, num_intersections=10, num_trainstation
nodes_per_col = int(np.ceil(tot_num_node / nodes_per_row)) nodes_per_col = int(np.ceil(tot_num_node / nodes_per_row))
x_positions = np.linspace(2, height - 2, nodes_per_row, dtype=int) x_positions = np.linspace(2, height - 2, nodes_per_row, dtype=int)
y_positions = np.linspace(2, width - 2, nodes_per_col, dtype=int) y_positions = np.linspace(2, width - 2, nodes_per_col, dtype=int)
for node_idx in range(num_cities + num_intersections): for node_idx in range(num_cities + num_intersections):
to_close = True to_close = True
tries = 0 tries = 0
...@@ -946,7 +900,7 @@ def sparse_rail_generator(num_cities=100, num_intersections=10, num_trainstation ...@@ -946,7 +900,7 @@ def sparse_rail_generator(num_cities=100, num_intersections=10, num_trainstation
delete_idx = np.where(available_cities == current_node) delete_idx = np.where(available_cities == current_node)
available_cities = np.delete(available_cities, delete_idx, 0) available_cities = np.delete(available_cities, delete_idx, 0)
elif len(available_intersections) > 0 and len(available_cities) > 0: elif current_node >= num_cities and len(available_cities) > 0:
available_nodes = available_cities available_nodes = available_cities
delete_idx = np.where(available_intersections == current_node) delete_idx = np.where(available_intersections == current_node)
available_intersections = np.delete(available_intersections, delete_idx, 0) available_intersections = np.delete(available_intersections, delete_idx, 0)
...@@ -1008,6 +962,37 @@ def sparse_rail_generator(num_cities=100, num_intersections=10, num_trainstation ...@@ -1008,6 +962,37 @@ def sparse_rail_generator(num_cities=100, num_intersections=10, num_trainstation
# Check if connection was made # Check if connection was made
if len(connection) == 0: if len(connection) == 0:
train_stations[trainstation_node].pop(-1) train_stations[trainstation_node].pop(-1)
# Place passing lanes at intersections
# We currently place them uniformly distirbuted among all cities
if enhance_intersection:
for intersection in range(num_intersections):
intersect_x_1 = np.clip(intersection_positions[intersection][0] + np.random.randint(1, 3),
1,
height - 2)
intersect_y_1 = np.clip(intersection_positions[intersection][1] + np.random.randint(-3, 3),
2,
width - 2)
intersect_x_2 = np.clip(
intersection_positions[intersection][0] + np.random.randint(-3, -1),
1,
height - 2)
intersect_y_2 = np.clip(
intersection_positions[intersection][1] + np.random.randint(-3, 3),
1,
width - 2)
# Connect train station to the correct node
connect_nodes(rail_trans, rail_array, (intersect_x_1, intersect_y_1),
(intersect_x_2, intersect_y_2))
connect_nodes(rail_trans, rail_array, intersection_positions[intersection],
(intersect_x_1, intersect_y_1))
connect_nodes(rail_trans, rail_array, intersection_positions[intersection],
(intersect_x_2, intersect_y_2))
grid_map.fix_transitions((intersect_x_1, intersect_y_1))
grid_map.fix_transitions((intersect_x_2, intersect_y_2))
# Fix all nodes with illegal transition maps # Fix all nodes with illegal transition maps
for current_node in node_positions: for current_node in node_positions:
grid_map.fix_transitions(current_node) grid_map.fix_transitions(current_node)
...@@ -1052,17 +1037,22 @@ def sparse_rail_generator(num_cities=100, num_intersections=10, num_trainstation ...@@ -1052,17 +1037,22 @@ def sparse_rail_generator(num_cities=100, num_intersections=10, num_trainstation
current_start_node = agent_start_targets_nodes[agent_idx][0] current_start_node = agent_start_targets_nodes[agent_idx][0]
current_target_node = agent_start_targets_nodes[agent_idx][1] current_target_node = agent_start_targets_nodes[agent_idx][1]
target_station_idx = np.random.randint(len(train_stations[current_target_node])) target_station_idx = np.random.randint(len(train_stations[current_target_node]))
start_station_idx = np.random.randint(len(train_stations[current_start_node]))
target = train_stations[current_target_node][target_station_idx] target = train_stations[current_target_node][target_station_idx]
start = train_stations[current_start_node][start_station_idx]
while (target[0], target[1]) in agents_target: while (target[0], target[1]) in agents_target:
target_station_idx = np.random.randint(len(train_stations[current_target_node])) target_station_idx = np.random.randint(len(train_stations[current_target_node]))
target = train_stations[current_target_node][target_station_idx] target = train_stations[current_target_node][target_station_idx]
while (start[0], start[1]) in agents_position: agents_target.append((target[0], target[1]))
for agent_idx in range(num_agents):
current_start_node = agent_start_targets_nodes[agent_idx][0]
current_target_node = agent_start_targets_nodes[agent_idx][1]
start_station_idx = np.random.randint(len(train_stations[current_start_node]))
start = train_stations[current_start_node][start_station_idx]
while (start[0], start[1]) in agents_position or (start[0], start[1]) in agents_target:
start_station_idx = np.random.randint(len(train_stations[current_start_node])) start_station_idx = np.random.randint(len(train_stations[current_start_node]))
start = train_stations[current_start_node][start_station_idx] start = train_stations[current_start_node][start_station_idx]
agents_position.append((start[0], start[1])) agents_position.append((start[0], start[1]))
agents_target.append((target[0], target[1]))
# Orient the agent correctly # Orient the agent correctly
for orientation in range(4): for orientation in range(4):
......
flatland/utils/graphics_pil.py
View file @ a9716d8a
...@@ -41,7 +41,7 @@ class PILGL(GraphicsLayer): ...@@ -41,7 +41,7 @@ class PILGL(GraphicsLayer):
SELECTED_AGENT_LAYER = 4 SELECTED_AGENT_LAYER = 4
SELECTED_TARGET_LAYER = 5 SELECTED_TARGET_LAYER = 5
def __init__(self, width, height, jupyter=False, screen_width=800,screen_height=600): def __init__(self, width, height, jupyter=False, screen_width=800, screen_height=600):
self.yxBase = (0, 0) self.yxBase = (0, 0)
self.linewidth = 4 self.linewidth = 4
self.n_agent_colors = 1 # overridden in loadAgent self.n_agent_colors = 1 # overridden in loadAgent
...@@ -263,9 +263,9 @@ class PILGL(GraphicsLayer): ...@@ -263,9 +263,9 @@ class PILGL(GraphicsLayer):
class PILSVG(PILGL): class PILSVG(PILGL):
def __init__(self, width, height, jupyter=False, screen_width=800,screen_height=600): def __init__(self, width, height, jupyter=False, screen_width=800, screen_height=600):
oSuper = super() oSuper = super()
oSuper.__init__(width, height, jupyter,screen_width,screen_height) oSuper.__init__(width, height, jupyter, screen_width, screen_height)
self.lwAgents = [] self.lwAgents = []
self.agents_prev = [] self.agents_prev = []
......
flatland/utils/rendertools.py
View file @ a9716d8a
...@@ -39,7 +39,8 @@ class RenderTool(object): ...@@ -39,7 +39,8 @@ class RenderTool(object):
theta = np.linspace(0, np.pi / 2, 5) theta = np.linspace(0, np.pi / 2, 5)
arc = array([np.cos(theta), np.sin(theta)]).T # from [1,0] to [0,1] arc = array([np.cos(theta), np.sin(theta)]).T # from [1,0] to [0,1]
def __init__(self, env, gl="PILSVG", jupyter=False, agent_render_variant=AgentRenderVariant.ONE_STEP_BEHIND, screen_width=800,screen_height=600): def __init__(self, env, gl="PILSVG", jupyter=False, agent_render_variant=AgentRenderVariant.ONE_STEP_BEHIND,
screen_width=800, screen_height=600):
self.env = env self.env = env
self.frame_nr = 0 self.frame_nr = 0
self.start_time = time.time() self.start_time = time.time()
...@@ -48,12 +49,12 @@ class RenderTool(object): ...@@ -48,12 +49,12 @@ class RenderTool(object):
self.agent_render_variant = agent_render_variant self.agent_render_variant = agent_render_variant
if gl == "PIL": if gl == "PIL":
self.gl = PILGL(env.width, env.height, jupyter, screen_width=screen_width,screen_height=screen_height) self.gl = PILGL(env.width, env.height, jupyter, screen_width=screen_width, screen_height=screen_height)
elif gl == "PILSVG": elif gl == "PILSVG":
self.gl = PILSVG(env.width, env.height, jupyter, screen_width=screen_width,screen_height=screen_height) self.gl = PILSVG(env.width, env.height, jupyter, screen_width=screen_width, screen_height=screen_height)
else: else:
print("[", gl, "] not found, switch to PILSVG") print("[", gl, "] not found, switch to PILSVG")
self.gl = PILSVG(env.width, env.height, jupyter, screen_width=screen_width,screen_height=screen_height) self.gl = PILSVG(env.width, env.height, jupyter, screen_width=screen_width, screen_height=screen_height)
self.new_rail = True self.new_rail = True
self.update_background() self.update_background()
......
tests/test_flatland_env_sparse_rail_generator.py
View file @ a9716d8a
import os
import time
import numpy as np import numpy as np
from flatland.envs.generators import sparse_rail_generator, realistic_rail_generator from flatland.envs.generators import sparse_rail_generator, realistic_rail_generator
...@@ -22,15 +19,6 @@ def test_realistic_rail_generator(vizualization_folder_name=None): ...@@ -22,15 +19,6 @@ def test_realistic_rail_generator(vizualization_folder_name=None):
screen_width=1600) screen_width=1600)
env_renderer.render_env(show=True, show_observations=True, show_predictions=False) env_renderer.render_env(show=True, show_observations=True, show_predictions=False)
if vizualization_folder_name is not None:
env_renderer.gl.save_image(
os.path.join(
vizualization_folder_name,
"flatland_frame_{:04d}.png".format(test_loop)
))
env_renderer.close_window()
def test_sparse_rail_generator(): def test_sparse_rail_generator():
env = RailEnv(width=50, env = RailEnv(width=50,
height=50, height=50,
...@@ -39,15 +27,13 @@ def test_sparse_rail_generator(): ...@@ -39,15 +27,13 @@ def test_sparse_rail_generator():
num_trainstations=50, # Number of possible start/targets on map num_trainstations=50, # Number of possible start/targets on map
min_node_dist=6, # Minimal distance of nodes min_node_dist=6, # Minimal distance of nodes
node_radius=3, # Proximity of stations to city center node_radius=3, # Proximity of stations to city center
num_neighb=4, # Number of connections to other cities num_neighb=3, # Number of connections to other cities
seed=5, # Random seed seed=5, # Random seed
realistic_mode=True # Ordered distribution of nodes
), ),
number_of_agents=45, number_of_agents=10,
obs_builder_object=GlobalObsForRailEnv()) obs_builder_object=GlobalObsForRailEnv())
# reset to initialize agents_static # reset to initialize agents_static
env_renderer = RenderTool(env, gl="PILSVG", ) env_renderer = RenderTool(env, gl="PILSVG", )
env_renderer.render_env(show=True, show_observations=True, show_predictions=False) env_renderer.render_env(show=True, show_observations=True, show_predictions=False)
time.sleep(2)
test_realistic_rail_generator(vizualization_folder_name="./rendering")
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