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from flatland.core.grid.grid_utils import Vec2dOperations as Vec2d
from flatland.envs.observations import GlobalObsForRailEnv
from flatland.envs.rail_env import RailEnv
from flatland.envs.rail_generators_city_generator import city_generator
from flatland.envs.schedule_generators import city_schedule_generator
from flatland.utils.rendertools import RenderTool, AgentRenderVariant
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os.mkdir("./../render_output/")
for itrials in np.arange(1, 1000, 1):
print(itrials, "generate new city")
# init seed
np.random.seed(itrials)
# select distance function used in a-star path finding
dist_fun = Vec2d.get_manhattan_distance
dfsel = (itrials - 1) % 3
if dfsel == 1:
dist_fun = Vec2d.get_euclidean_distance
elif dfsel == 2:
dist_fun = Vec2d.get_chebyshev_distance
# create RailEnv and use the city_generator to create a map
env = RailEnv(width=40 + np.random.choice(100),
height=40 + np.random.choice(100),
rail_generator=city_generator(num_cities=5 + np.random.choice(10),
city_size=10 + np.random.choice(5),
allowed_rotation_angles=np.arange(0, 360, 6),
max_number_of_station_tracks=4 + np.random.choice(4),
nbr_of_switches_per_station_track=2 + np.random.choice(2),
connect_max_nbr_of_shortes_city=2 + np.random.choice(4),
do_random_connect_stations=itrials % 2 == 0,
a_star_distance_function=dist_fun,
seed=itrials,
print_out_info=False
),
schedule_generator=city_schedule_generator(),
number_of_agents=10000,
obs_builder_object=GlobalObsForRailEnv())
# reset to initialize agents_static
env_renderer = RenderTool(env, gl="PILSVG", screen_width=1400, screen_height=1000,
agent_render_variant=AgentRenderVariant.AGENT_SHOWS_OPTIONS_AND_BOX)
env_renderer.render_env(show=True, show_observations=False, show_predictions=False)
# store rendered file into render_output if the path exists
env_renderer.gl.save_image(
os.path.join(
"./../render_output/",
"flatland_frame_{:04d}.png".format(itrials)
))
# close the renderer / window
env_renderer.close_window()