def main():
parser = argparse.ArgumentParser('Parse configuration file')
parser.add_argument('--env_config',
type=str,
default='data/output/env.config')
parser.add_argument('--policy_config',
type=str,
default='data/output/policy.config')
parser.add_argument('--policy', type=str, default='scr')
parser.add_argument('--model_dir', type=str, default=None)
parser.add_argument('--il', default=False, action='store_true')
parser.add_argument('--gpu', default=False, action='store_true')
parser.add_argument('--visualize', default=False, action='store_true')
parser.add_argument('--phase', type=str, default='test')
parser.add_argument('--test_case', type=int, default=None)
parser.add_argument('--square', default=False, action='store_true')
parser.add_argument('--circle', default=False, action='store_true')
parser.add_argument('--video_file', type=str, default=None)
parser.add_argument('--plot_file', type=str, default=None)
args = parser.parse_args()
if args.model_dir is not None:
env_config_file = os.path.join(args.model_dir,
os.path.basename(args.env_config))
policy_config_file = os.path.join(args.model_dir,
os.path.basename(args.policy_config))
if args.il:
model_weights = os.path.join(args.model_dir, 'il_model.pth')
else:
if os.path.exists(
os.path.join(args.model_dir, 'resumed_rl_model.pth')):
model_weights = os.path.join(args.model_dir,
'resumed_rl_model.pth')
else:
model_weights = os.path.join(args.model_dir, 'rl_model.pth')
else:
policy_config_file = args.env_config
# configure logging and device
logging.basicConfig(level=logging.INFO,
format='%(asctime)s, %(levelname)s: %(message)s',
datefmt="%Y-%m-%d %H:%M:%S")
device = torch.device(
"cuda:0" if torch.cuda.is_available() and args.gpu else "cpu")
logging.info('Using device: %s', device)
# configure policy
policy = policy_factory[args.policy]()
policy_config = configparser.RawConfigParser()
policy_config.read(policy_config_file)
policy.configure(policy_config)
if policy.trainable:
if args.model_dir is None:
parser.error(
'Trainable policy must be specified with a model weights directory'
)
policy.get_model().load_state_dict(torch.load(model_weights))
# configure environment
env = gym.make('CrowdSim-v0')
env.configure(args.env_config)
robot = Robot()
robot.configure(args.env_config, 'robot')
robot.set_policy(policy)
env.set_robot(robot)
humans = [Human() for _ in range(env.human_num)]
for human in humans:
human.configure(args.env_config, 'humans')
env.set_humans(humans)
if args.square:
env.test_sim = 'square_crossing'
if args.circle:
env.test_sim = 'circle_crossing'
explorer = Explorer(env, robot, device, gamma=0.9)
policy.set_phase(args.phase)
policy.set_device(device)
# set safety space for ORCA in non-cooperative simulation
if isinstance(robot.policy, ORCA):
if robot.visible:
robot.policy.safety_space = 0
else:
robot.policy.safety_space = 0
logging.info('ORCA agent buffer: %f', robot.policy.safety_space)
policy.set_env(env)
robot.print_info()
explorer.run_k_episodes(env.case_size[args.phase],
args.phase,
print_failure=True)
def main():
parser = argparse.ArgumentParser('Parse configuration file')
parser.add_argument('--env_config', type=str, default='configs/env.config')
parser.add_argument('--policy', type=str, default='scr')
parser.add_argument('--policy_config',
type=str,
default='configs/policy.config')
parser.add_argument('--train_config',
type=str,
default='configs/train.config')
parser.add_argument('--output_dir', type=str, default='data/output')
parser.add_argument('--weights', type=str)
parser.add_argument('--resume', default=False, action='store_true')
parser.add_argument('--gpu', default=False, action='store_true')
parser.add_argument('--debug', default=False, action='store_true')
args = parser.parse_args()
# configure paths
make_new_dir = True
if os.path.exists(args.output_dir):
key = input(
'Output directory already exists! Overwrite the folder? (y/n)')
if key == 'y' and not args.resume:
shutil.rmtree(args.output_dir)
else:
make_new_dir = False
args.env_config = os.path.join(args.output_dir,
os.path.basename(args.env_config))
args.policy_config = os.path.join(
args.output_dir, os.path.basename(args.policy_config))
args.train_config = os.path.join(
args.output_dir, os.path.basename(args.train_config))
if make_new_dir:
os.makedirs(args.output_dir)
shutil.copy(args.env_config, args.output_dir)
shutil.copy(args.policy_config, args.output_dir)
shutil.copy(args.train_config, args.output_dir)
log_file = os.path.join(args.output_dir, 'output.log')
il_weight_file = os.path.join(args.output_dir, 'il_model.pth')
rl_weight_file = os.path.join(args.output_dir, 'rl_model.pth')
# configure logging
mode = 'a' if args.resume else 'w'
file_handler = logging.FileHandler(log_file, mode=mode)
stdout_handler = logging.StreamHandler(sys.stdout)
level = logging.INFO if not args.debug else logging.DEBUG
logging.basicConfig(level=level,
handlers=[stdout_handler, file_handler],
format='%(asctime)s, %(levelname)s: %(message)s',
datefmt="%Y-%m-%d %H:%M:%S")
repo = git.Repo(search_parent_directories=True)
logging.info('Current git head hash code: %s'.format(
repo.head.object.hexsha))
device = torch.device(
"cuda:0" if torch.cuda.is_available() and args.gpu else "cpu")
logging.info('Using device: %s', device)
# configure policy
policy = policy_factory[args.policy]()
if not policy.trainable:
parser.error('Policy has to be trainable')
if args.policy_config is None:
parser.error(
'Policy config has to be specified for a trainable network')
policy_config = configparser.RawConfigParser()
policy_config.read(args.policy_config)
policy.configure(policy_config)
policy.set_device(device)
# configure environment
env = gym.make('CrowdSim-v0')
env.configure(args.env_config)
robot = Robot()
robot.configure(args.env_config, 'robot')
env.set_robot(robot)
humans = [Human() for _ in range(env.human_num)]
for human in humans:
human.configure(args.env_config, 'humans')
env.set_humans(humans)
# read training parameters
if args.train_config is None:
parser.error(
'Train config has to be specified for a trainable network')
train_config = configparser.RawConfigParser()
train_config.read(args.train_config)
rl_learning_rate = train_config.getfloat('train', 'rl_learning_rate')
train_batches = train_config.getint('train', 'train_batches')
train_episodes = train_config.getint('train', 'train_episodes')
sample_episodes = train_config.getint('train', 'sample_episodes')
target_update_interval = train_config.getint('train',
'target_update_interval')
evaluation_interval = train_config.getint('train', 'evaluation_interval')
capacity = train_config.getint('train', 'capacity')
epsilon_start = train_config.getfloat('train', 'epsilon_start')
epsilon_end = train_config.getfloat('train', 'epsilon_end')
epsilon_decay = train_config.getfloat('train', 'epsilon_decay')
checkpoint_interval = train_config.getint('train', 'checkpoint_interval')
# configure trainer and explorer
memory = ReplayMemory(capacity)
model = policy.get_model()
batch_size = train_config.getint('trainer', 'batch_size')
trainer = Trainer(policy, memory, device, batch_size)
explorer = Explorer(env,
robot,
device,
memory,
policy.gamma,
target_policy=policy)
# imitation learning
if args.resume:
if not os.path.exists(rl_weight_file):
logging.error('RL weights does not exist')
model.load_state_dict(torch.load(rl_weight_file))
rl_weight_file = os.path.join(args.output_dir, 'resumed_rl_model.pth')
logging.info(
'Load reinforcement learning trained weights. Resume training')
elif os.path.exists(il_weight_file):
model.load_state_dict(torch.load(il_weight_file))
logging.info('Load imitation learning trained weights.')
else:
il_episodes = train_config.getint('imitation_learning', 'il_episodes')
il_policy = train_config.get('imitation_learning', 'il_policy')
il_epochs = train_config.getint('imitation_learning', 'il_epochs')
il_learning_rate = train_config.getfloat('imitation_learning',
'il_learning_rate')
trainer.set_learning_rate(il_learning_rate)
if robot.visible:
safety_space = 0
else:
safety_space = train_config.getfloat('imitation_learning',
'safety_space')
il_policy = policy_factory[il_policy]()
il_policy.multiagent_training = policy.multiagent_training
il_policy.safety_space = safety_space
robot.set_policy(il_policy)
explorer.run_k_episodes(il_episodes,
'train',
update_memory=True,
imitation_learning=True)
trainer.optimize_epoch(il_epochs)
torch.save(model.state_dict(), il_weight_file)
logging.info('Finish imitation learning. Weights saved.')
logging.info('Experience set size: %d/%d', len(memory),
memory.capacity)
explorer.update_target_model(model)
# reinforcement learning
policy.set_env(env)
robot.set_policy(policy)
robot.print_info()
trainer.set_learning_rate(rl_learning_rate)
# fill the memory pool with some RL experience
if args.resume:
robot.policy.set_epsilon(epsilon_end)
explorer.run_episode('val', video_file=f'data/output/video_e{-1}.mp4')
explorer.run_k_episodes(100, 'train', update_memory=True, episode=0)
logging.info('Experience set size: %d/%d', len(memory),
memory.capacity)
episode = 0
while episode < train_episodes:
if args.resume:
epsilon = epsilon_end
else:
if episode < epsilon_decay:
epsilon = epsilon_start + (
epsilon_end - epsilon_start) / epsilon_decay * episode
else:
epsilon = epsilon_end
robot.policy.set_epsilon(epsilon)
# sample k episodes into memory and optimize over the generated memory
explorer.run_k_episodes(sample_episodes,
'train',
update_memory=True,
episode=episode)
trainer.optimize_batch(train_batches)
# evaluate the model
if episode % evaluation_interval == 0:
explorer.run_episode(
'val', video_file=f'data/output/video_e{episode}.mp4')
episode += 1
if episode % target_update_interval == 0:
explorer.update_target_model(model)
if episode != 0 and episode % checkpoint_interval == 0:
torch.save(model.state_dict(), rl_weight_file)
# final test
explorer.run_k_episodes(env.case_size['test'], 'test', episode=episode)
def main():
parser = argparse.ArgumentParser('Parse configuration file')
parser.add_argument('--env_config', type=str, default='data/output/env.config')
parser.add_argument('--policy_config', type=str, default='data/output/policy.config')
parser.add_argument('--policy', type=str, default='scr')
parser.add_argument('--model_dir', type=str, default=None)
parser.add_argument('--il', default=False, action='store_true')
parser.add_argument('--gpu', default=False, action='store_true')
parser.add_argument('--visualize', default=False, action='store_true')
parser.add_argument('--phase', type=str, default='test')
parser.add_argument('--test_case', type=int, default=None)
parser.add_argument('--square', default=False, action='store_true')
parser.add_argument('--circle', default=False, action='store_true')
parser.add_argument('--video_file', type=str, default=None)
parser.add_argument('--plot_file', type=str, default=None)
args = parser.parse_args()
if args.model_dir is not None:
env_config_file = os.path.join(args.model_dir, os.path.basename(args.env_config))
policy_config_file = os.path.join(args.model_dir, os.path.basename(args.policy_config))
if args.il:
model_weights = os.path.join(args.model_dir, 'il_model.pth')
else:
if os.path.exists(os.path.join(args.model_dir, 'resumed_rl_model.pth')):
model_weights = os.path.join(args.model_dir, 'resumed_rl_model.pth')
else:
model_weights = os.path.join(args.model_dir, 'rl_model.pth')
else:
policy_config_file = args.env_config
# configure logging and device
logging.basicConfig(level=logging.INFO, format='%(asctime)s, %(levelname)s: %(message)s',
datefmt="%Y-%m-%d %H:%M:%S")
device = torch.device("cuda:0" if torch.cuda.is_available() and args.gpu else "cpu")
logging.info('Using device: %s', device)
# configure policy
policy = policy_factory[args.policy]()
policy_config = configparser.RawConfigParser()
policy_config.read(policy_config_file)
policy.configure(policy_config)
if policy.trainable:
if args.model_dir is None:
parser.error('Trainable policy must be specified with a model weights directory')
policy.get_model().load_state_dict(torch.load(model_weights))
# configure environment
env = gym.make('CrowdSim-v0')
env.configure(args.env_config)
robot = Robot()
robot.configure(args.env_config, 'robot')
robot.set_policy(policy)
env.set_robot(robot)
humans = [Human() for _ in range(env.human_num)]
for human in humans:
human.configure(args.env_config, 'humans')
env.set_humans(humans)
if args.square:
env.test_sim = 'square_crossing'
if args.circle:
env.test_sim = 'circle_crossing'
explorer = Explorer(env, robot, device, gamma=0.9)
policy.set_phase(args.phase)
policy.set_device(device)
# set safety space for ORCA in non-cooperative simulation
if isinstance(robot.policy, ORCA):
if robot.visible:
robot.policy.safety_space = 0
else:
robot.policy.safety_space = 0
logging.info('ORCA agent buffer: %f', robot.policy.safety_space)
policy.set_env(env)
robot.print_info()
ob = env.reset(args.phase, args.test_case)
done = False
last_pos = np.array(robot.get_position())
observation_subscribers = []
if args.plot_file:
plotter = Plotter(args.plot_file)
observation_subscribers.append(plotter)
if args.video_file:
video = Video(args.video_file)
observation_subscribers.append(video)
t = 0
while not done:
action = robot.act(ob)
ob, _, done, info = env.step(action)
notify(observation_subscribers, env.state)
if args.visualize:
env.render()
plt.pause(.001)
current_pos = np.array(robot.get_position())
logging.debug('Speed: %.2f', np.linalg.norm(current_pos - last_pos) / robot.time_step)
last_pos = current_pos
t += 1
if args.plot_file:
plotter.save()
if args.video_file:
video.make([robot.policy.draw_attention, robot.policy.draw_observation])
logging.info('It takes %.2f seconds to finish. Final status is %s', env.global_time, info)
if robot.visible and info == 'reach goal':
human_times = env.get_human_times()
logging.info('Average time for humans to reach goal: %.2f', sum(human_times) / len(human_times))