def main():
args, params = parse_args_tmax(AgentTMAX.Params)
env_id = args.env
global key_to_action
if 'dmlab' in env_id:
from utils.envs.dmlab import play_dmlab
key_to_action = play_dmlab.key_to_action
elif 'atari' in env_id:
key_to_action = atari_utils.key_to_action
elif 'doom' in env_id:
key_to_action = doom_utils.key_to_action
else:
raise Exception('Unknown env')
# start keypress listener (to pause/resume execution or exit)
def start_listener():
with Listener(on_press=on_press, on_release=on_release) as listener:
listener.join()
listener_thread = Thread(target=start_listener)
listener_thread.start()
status = build_graph(params, args.env)
if not terminate:
log.debug('Press ESC to exit...')
listener_thread.join()
return status
def main():
args, params = parse_args_tmax(AgentTMAX.Params)
env_id = args.env
global key_to_action
if 'dmlab' in env_id:
from utils.envs.dmlab import play_dmlab
key_to_action = play_dmlab.key_to_action
elif 'atari' in env_id:
key_to_action = atari_utils.key_to_action
elif 'doom' in env_id:
key_to_action = doom_utils.key_to_action
else:
raise Exception('Unknown env')
try:
show_map = args.show_automap
except AttributeError:
show_map = False
# start keypress listener (to pause/resume execution or exit)
def start_listener():
with Listener(on_press=on_press, on_release=on_release) as listener:
listener.join()
listener_thread = Thread(target=start_listener)
listener_thread.start()
status = enjoy(params, args.env, show_automap=show_map)
log.debug('Press ESC to exit...')
listener_thread.join()
return status
def tmax_train_run(self, env_name=None):
test_dir_name = self.__class__.__name__
args, params = parse_args_tmax(AgentTMAX.Params, argv=[])
if env_name is not None:
args.env = env_name
params.experiments_root = test_dir_name
params.num_envs = 16
params.train_for_steps = 60
params.initial_save_rate = 20
params.batch_size = 32
params.ppo_epochs = 2
params.distance_bootstrap = 10
params.stage_duration = 100
params.distance_encoder = 'resnet'
params.locomotion_encoder = 'resnet'
tmax_train_dir = join(experiments_dir(), params.experiments_root)
ensure_dir_exists(tmax_train_dir)
shutil.rmtree(tmax_train_dir)
status = train(params, args.env)
self.assertEqual(status, TrainStatus.SUCCESS)
root_dir = params.experiment_dir()
self.assertTrue(os.path.isdir(root_dir))
enjoy(params, args.env, max_num_episodes=1, max_num_frames=50)
shutil.rmtree(tmax_train_dir)
self.assertFalse(os.path.isdir(root_dir))
def test_locomotion(self):
g = tf.Graph()
env = make_doom_env(doom_env_by_name(TEST_ENV_NAME))
args, params = parse_args_tmax(AgentTMAX.Params, argv=[])
with g.as_default():
locomotion_net = LocomotionNetwork(env, params)
obs = env.reset()
with tf.Session(graph=g) as sess:
sess.run(tf.global_variables_initializer())
action = locomotion_net.navigate(sess, [obs], [obs], [obs])[0]
self.assertGreaterEqual(action, 0)
self.assertLess(action, env.action_space.n)
env.close()
g.finalize()
def main():
args, params = parse_args_tmax(AgentTMAX.Params)
status = train_distance(params, args.env)
return status
def main():
args, params = parse_args_tmax(AgentTMAX.Params)
status = train_locomotion(params, args.env)
return status
def main():
"""Script entry point."""
args, params = parse_args_tmax(AgentTMAX.Params)
return train(params, args.env)
def main():
args, params = parse_args_tmax(AgentTMAX.Params)
status = trajectory_to_map(params, args.env)
return status
