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)