def demo_small_map_wall_penalty(level_script):
print("Loading deepmind_lab_gym from %s" % dlg.__file__)
env = dlg.register_and_make(level_script
, dict(width=320, height=320, fps=30)
, dlg.ActionMapper("discrete")
, wall_penalty_max_dist = 30
, wall_penalty_max = 0.2)
run_env_interactively(env)
def demo_small_map_test_mode(level_script):
print("Loading deepmind_lab_gym from %s" % dlg.__file__)
env = dlg.register_and_make(level_script
, dict(width=320, height=320, fps=30)
, dlg.ActionMapper("discrete")
, additional_observation_types=[
"GOAL.LOC", "POSE", "GOAL.FOUND"])
run_env_interactively(env)
def demo_discrete_big_steps(level_script):
print("Loading deepmind_lab_gym from %s" % dlg.__file__)
env = dlg.register_and_make(level_script
, dict(width=320, height=320, fps=30)
, dlg.ManhattanWorldActionMapper_v0
, additional_observation_types = ['POSE']
, init_game_seed = 0)
run_env_interactively2(env)
def demo_small_star_map_continuous_spawn():
print("Loading deepmind_lab_gym from %s" % dlg.__file__)
level_script = "small_star_map_continuous_spawn_01"
env = dlg.register_and_make(level_script
, dict(width=320, height=320, fps=30
, noclip="true")
, dlg.ActionMapper("discrete")
, additional_observation_types=["GOAL.LOC",
"POSE", "GOAL.FOUND"])
run_env_interactively(env)
def demo_random_mazes(level_script="tests/demo_map",
multiproc=True,
multiproc_use_threads=False):
env = dlg.register_and_make(level_script,
dict(width=84, height=84, fps=30),
dlg.ActionMapper("discrete"),
additional_observation_types=[])
if multiproc:
env = MultiProcGym(env, 3, use_threads=multiproc_use_threads)
start_time = time.time()
for i in range(600 * 30):
obs, reward, terminal, info = env.step(env.action_space.sample())
print("\r{}".format(i), end='')
if terminal:
print("env.reset() time_take = {}".format(time.time() -
start_time))
start_time = time.time()
env.reset()
def demo_random_mazes(level_script='random_mazes'
, rows=9
, cols=9
, mode='training'
, num_maps = 100):
env = dlg.register_and_make(level_script
, dict(width=320, height=320, fps=30
, rows = rows
, cols = cols
, mode = mode
, num_maps = num_maps
, random_spawn_random_goal = "True"
, apple_prob = 0.1
, episode_length_seconds = 20)
, dlg.ActionMapper("discrete")
, additional_observation_types=[
"GOAL.LOC", "POSE", "GOAL.FOUND"]
, entry_point_object=mpdmlab.RandomMazesDMLab)
run_env_interactively2(env)