def main(args):
# configure logging and device
level = logging.DEBUG if args.debug else logging.INFO
logging.basicConfig(level=level, 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)
if args.model_dir is not None:
if args.config is not None:
config_file = args.config
else:
config_file = os.path.join(args.model_dir, 'config.py')
if args.il:
model_weights = os.path.join(args.model_dir, 'il_model.pth')
logging.info('Loaded IL weights')
elif args.rl:
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:
print(os.listdir(args.model_dir))
model_weights = os.path.join(args.model_dir, sorted(os.listdir(args.model_dir))[-1])
logging.info('Loaded RL weights')
else:
model_weights = os.path.join(args.model_dir, 'best_val.pth')
logging.info('Loaded RL weights with best VAL')
else:
config_file = args.config
spec = importlib.util.spec_from_file_location('config', config_file)
if spec is None:
parser.error('Config file not found.')
config = importlib.util.module_from_spec(spec)
spec.loader.exec_module(config)
# configure policy
policy_config = config.PolicyConfig(args.debug)
policy = policy_factory[policy_config.name]()
if args.planning_depth is not None:
policy_config.model_predictive_rl.do_action_clip = True
policy_config.model_predictive_rl.planning_depth = args.planning_depth
if args.planning_width is not None:
policy_config.model_predictive_rl.do_action_clip = True
policy_config.model_predictive_rl.planning_width = args.planning_width
if args.sparse_search:
policy_config.model_predictive_rl.sparse_search = True
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.load_model(model_weights)
# configure environment
env_config = config.EnvConfig(args.debug)
if args.human_num is not None:
env_config.sim.human_num = args.human_num
env = gym.make('CrowdSim-v0')
env.configure(env_config)
if args.square:
env.test_scenario = 'square_crossing'
if args.circle:
env.test_scenario = 'circle_crossing'
if args.test_scenario is not None:
env.test_scenario = args.test_scenario
robot = Robot(env_config, 'robot')
env.set_robot(robot)
robot.time_step = env.time_step
robot.set_policy(policy)
explorer = Explorer(env, robot, device, None, gamma=0.9)
train_config = config.TrainConfig(args.debug)
epsilon_end = train_config.train.epsilon_end
if not isinstance(robot.policy, ORCA):
robot.policy.set_epsilon(epsilon_end)
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 = args.safety_space
else:
robot.policy.safety_space = args.safety_space
logging.info('ORCA agent buffer: %f', robot.policy.safety_space)
policy.set_env(env)
robot.print_info()
if args.visualize:
rewards = []
ob = env.reset(args.phase, args.test_case)
done = False
last_pos = np.array(robot.get_position())
while not done:
action = robot.act(ob)
ob, _, done, info = env.step(action)
rewards.append(_)
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
gamma = 0.9
cumulative_reward = sum([pow(gamma, t * robot.time_step * robot.v_pref)
* reward for t, reward in enumerate(rewards)])
if args.traj:
env.render('traj', args.video_file)
else:
if args.video_dir is not None:
if policy_config.name == 'gcn':
args.video_file = os.path.join(args.video_dir, policy_config.name + '_' + policy_config.gcn.similarity_function)
else:
args.video_file = os.path.join(args.video_dir, policy_config.name)
args.video_file = args.video_file + '_' + args.phase + '_' + str(args.test_case) + '.mp4'
env.render('video', args.video_file)
logging.info('It takes %.2f seconds to finish. Final status is %s, cumulative_reward is %f', env.global_time, info, cumulative_reward)
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))
else:
explorer.run_k_episodes(env.case_size[args.phase], args.phase, print_failure=True)
if args.plot_test_scenarios_hist:
test_angle_seeds = np.array(env.test_scene_seeds)
b = [i * 0.01 for i in range(101)]
n, bins, patches = plt.hist(test_angle_seeds, b, facecolor='g')
plt.savefig(os.path.join(args.model_dir, 'test_scene_hist.png'))
plt.close()
def main(args):
set_random_seeds(args.randomseed)
# configure paths
make_new_dir = True
if os.path.exists(args.output_dir):
if args.overwrite:
shutil.rmtree(args.output_dir)
else:
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
if make_new_dir:
os.makedirs(args.output_dir)
shutil.copy(args.config, os.path.join(args.output_dir, 'config.py'))
# # insert the arguments from command line to the config file
# with open(os.path.join(args.output_dir, 'config.py'), 'r') as fo:
# config_text = fo.read()
# search_pairs = {r"gcn.X_dim = \d*": "gcn.X_dim = {}".format(args.X_dim),
# r"gcn.num_layer = \d": "gcn.num_layer = {}".format(args.layers),
# r"gcn.similarity_function = '\w*'": "gcn.similarity_function = '{}'".format(args.sim_func),
# r"gcn.layerwise_graph = \w*": "gcn.layerwise_graph = {}".format(args.layerwise_graph),
# r"gcn.skip_connection = \w*": "gcn.skip_connection = {}".format(args.skip_connection)}
#
# for find, replace in search_pairs.items():
# config_text = re.sub(find, replace, config_text)
#
# with open(os.path.join(args.output_dir, 'config.py'), 'w') as fo:
# fo.write(config_text)
args.config = os.path.join(args.output_dir, 'config.py')
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')
spec = importlib.util.spec_from_file_location('config', args.config)
if spec is None:
parser.error('Config file not found.')
config = importlib.util.module_from_spec(spec)
spec.loader.exec_module(config)
# 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: {}'.format(
repo.head.object.hexsha))
logging.info('Current config content is :{}'.format(config))
device = torch.device(
"cuda:0" if torch.cuda.is_available() and args.gpu else "cpu")
logging.info('Using device: %s', device)
writer = SummaryWriter(log_dir=args.output_dir)
# configure policy
policy_config = config.PolicyConfig()
policy = policy_factory[policy_config.name]()
if not policy.trainable:
parser.error('Policy has to be trainable')
policy.configure(policy_config)
policy.set_device(device)
# configure environment
env_config = config.EnvConfig(args.debug)
env = gym.make('CrowdSim-v0')
env.configure(env_config)
robot = Robot(env_config, 'robot')
robot.time_step = env.time_step
env.set_robot(robot)
# read training parameters
train_config = config.TrainConfig(args.debug)
rl_learning_rate = train_config.train.rl_learning_rate
train_batches = train_config.train.train_batches
train_episodes = train_config.train.train_episodes
sample_episodes = train_config.train.sample_episodes
target_update_interval = train_config.train.target_update_interval
evaluation_interval = train_config.train.evaluation_interval
capacity = train_config.train.capacity
epsilon_start = train_config.train.epsilon_start
epsilon_end = train_config.train.epsilon_end
epsilon_decay = train_config.train.epsilon_decay
checkpoint_interval = train_config.train.checkpoint_interval
# configure trainer and explorer
memory = ReplayMemory(capacity)
model = policy.get_model()
batch_size = train_config.trainer.batch_size
optimizer = train_config.trainer.optimizer
if policy_config.name == 'model_predictive_rl':
trainer = MPRLTrainer(
model,
policy.state_predictor,
memory,
device,
policy,
writer,
batch_size,
optimizer,
env.human_num,
reduce_sp_update_frequency=train_config.train.
reduce_sp_update_frequency,
freeze_state_predictor=train_config.train.freeze_state_predictor,
detach_state_predictor=train_config.train.detach_state_predictor,
share_graph_model=policy_config.model_predictive_rl.
share_graph_model)
else:
trainer = VNRLTrainer(model, memory, device, policy, batch_size,
optimizer, writer)
explorer = Explorer(env,
robot,
device,
writer,
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.imitation_learning.il_episodes
il_policy = train_config.imitation_learning.il_policy
il_epochs = train_config.imitation_learning.il_epochs
il_learning_rate = train_config.imitation_learning.il_learning_rate
trainer.set_learning_rate(il_learning_rate)
if robot.visible:
safety_space = 0
else:
safety_space = train_config.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)
policy.save_model(il_weight_file)
logging.info('Finish imitation learning. Weights saved.')
logging.info('Experience set size: %d/%d', len(memory),
memory.capacity)
trainer.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_k_episodes(100, 'train', update_memory=True, episode=0)
logging.info('Experience set size: %d/%d', len(memory),
memory.capacity)
episode = 0
best_val_reward = -1
best_val_model = None
# evaluate the model after imitation learning
if episode % evaluation_interval == 0:
logging.info(
'Evaluate the model instantly after imitation learning on the validation cases'
)
explorer.run_k_episodes(env.case_size['val'], 'val', episode=episode)
explorer.log('val', episode // evaluation_interval)
if args.test_after_every_eval:
explorer.run_k_episodes(env.case_size['test'],
'test',
episode=episode,
print_failure=True)
explorer.log('test', episode // evaluation_interval)
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)
explorer.log('train', episode)
trainer.optimize_batch(train_batches, episode)
episode += 1
if episode % target_update_interval == 0:
trainer.update_target_model(model)
# evaluate the model
if episode % evaluation_interval == 0:
_, _, _, reward, _ = explorer.run_k_episodes(env.case_size['val'],
'val',
episode=episode)
explorer.log('val', episode // evaluation_interval)
if episode % checkpoint_interval == 0 and reward > best_val_reward:
best_val_reward = reward
best_val_model = copy.deepcopy(policy.get_state_dict())
# test after every evaluation to check how the generalization performance evolves
if args.test_after_every_eval:
explorer.run_k_episodes(env.case_size['test'],
'test',
episode=episode,
print_failure=True)
explorer.log('test', episode // evaluation_interval)
if episode != 0 and episode % checkpoint_interval == 0:
current_checkpoint = episode // checkpoint_interval - 1
save_every_checkpoint_rl_weight_file = rl_weight_file.split(
'.')[0] + '_' + str(current_checkpoint) + '.pth'
policy.save_model(save_every_checkpoint_rl_weight_file)
# # test with the best val model
if best_val_model is not None:
policy.load_state_dict(best_val_model)
torch.save(best_val_model, os.path.join(args.output_dir,
'best_val.pth'))
logging.info('Save the best val model with the reward: {}'.format(
best_val_reward))
explorer.run_k_episodes(env.case_size['test'],
'test',
episode=episode,
print_failure=True)
def main(args):
set_random_seeds(args.randomseed)
# configure paths
make_new_dir = True
if os.path.exists(args.output_dir):
if args.overwrite:
shutil.rmtree(args.output_dir)
else:
shutil.rmtree(args.output_dir)
if make_new_dir:
os.makedirs(args.output_dir)
shutil.copy(args.config, os.path.join(args.output_dir, 'config.py'))
# # insert the arguments from command line to the config file
# with open(os.path.join(args.output_dir, 'config.py'), 'r') as fo:
# config_text = fo.read()
# search_pairs = {r"gcn.X_dim = \d*": "gcn.X_dim = {}".format(args.X_dim),
# r"gcn.num_layer = \d": "gcn.num_layer = {}".format(args.layers),
# r"gcn.similarity_function = '\w*'": "gcn.similarity_function = '{}'".format(args.sim_func),
# r"gcn.layerwise_graph = \w*": "gcn.layerwise_graph = {}".format(args.layerwise_graph),
# r"gcn.skip_connection = \w*": "gcn.skip_connection = {}".format(args.skip_connection)}
#
# for find, replace in search_pairs.items():
# config_text = re.sub(find, replace, config_text)
#
# with open(os.path.join(args.output_dir, 'config.py'), 'w') as fo:
# fo.write(config_text)
args.config = os.path.join(args.output_dir, 'config.py')
log_file = os.path.join(args.output_dir, 'output.log')
in_weight_file = os.path.join(args.output_dir, 'in_model.pth')
il_weight_file = os.path.join(args.output_dir, 'il_model.pth')
rl_weight_file = os.path.join(args.output_dir, 'rl_model.pth')
spec = importlib.util.spec_from_file_location('config', args.config)
if spec is None:
parser.error('Config file not found.')
config = importlib.util.module_from_spec(spec)
spec.loader.exec_module(config)
# 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: {}'.format(
repo.head.object.hexsha))
logging.info('Current random seed: {}'.format(sys_args.randomseed))
logging.info('Current safe_weight: {}'.format(sys_args.safe_weight))
logging.info('Current goal_weight: {}'.format(sys_args.goal_weight))
logging.info('Current re_collision: {}'.format(sys_args.re_collision))
logging.info('Current re_arrival: {}'.format(sys_args.re_arrival))
logging.info('Current config content is :{}'.format(config))
device = torch.device(
"cuda:0" if torch.cuda.is_available() and args.gpu else "cpu")
logging.info('Using device: %s', device)
writer = SummaryWriter(log_dir=args.output_dir)
# configure policy
policy_config = config.PolicyConfig()
policy = policy_factory[policy_config.name]()
if not policy.trainable:
parser.error('Policy has to be trainable')
policy.configure(policy_config, device)
policy.set_device(device)
# configure environment
env_config = config.EnvConfig(args.debug)
env_config.reward.collision_penalty = args.re_collision
env_config.reward.success_reward = args.re_arrival
env_config.reward.goal_factor = args.goal_weight
env_config.reward.discomfort_penalty_factor = args.safe_weight
env_config.sim.human_num = args.human_num
env = gym.make('CrowdSim-v0')
env.configure(env_config)
robot = Robot(env_config, 'robot')
robot.time_step = env.time_step
env.set_robot(robot)
reward_estimator = Reward_Estimator()
reward_estimator.configure(env_config)
policy.reward_estimator = reward_estimator
# for continous action
action_dim = env.action_space.shape[0]
max_action = env.action_space.high
min_action = env.action_space.low
# read training parameters
train_config = config.TrainConfig(args.debug)
rl_learning_rate = train_config.train.rl_learning_rate
train_batches = train_config.train.train_batches
train_episodes = train_config.train.train_episodes
sample_episodes = train_config.train.sample_episodes
target_update_interval = train_config.train.target_update_interval
evaluation_interval = train_config.train.evaluation_interval
capacity = train_config.train.capacity
epsilon_start = train_config.train.epsilon_start
epsilon_end = train_config.train.epsilon_end
epsilon_decay = train_config.train.epsilon_decay
checkpoint_interval = train_config.train.checkpoint_interval
# configure trainer and explorer
memory = ReplayMemory(capacity)
model = policy.get_model()
batch_size = train_config.trainer.batch_size
optimizer = train_config.trainer.optimizer
print(policy_config.name)
if policy_config.name == 'model_predictive_rl':
trainer = MPRLTrainer(
model,
policy.state_predictor,
memory,
device,
policy,
writer,
batch_size,
optimizer,
env.human_num,
reduce_sp_update_frequency=train_config.train.
reduce_sp_update_frequency,
freeze_state_predictor=train_config.train.freeze_state_predictor,
detach_state_predictor=train_config.train.detach_state_predictor,
share_graph_model=policy_config.model_predictive_rl.
share_graph_model)
elif policy_config.name == 'tree_search_rl':
trainer = TSRLTrainer(
model,
policy.state_predictor,
memory,
device,
policy,
writer,
batch_size,
optimizer,
env.human_num,
reduce_sp_update_frequency=train_config.train.
reduce_sp_update_frequency,
freeze_state_predictor=train_config.train.freeze_state_predictor,
detach_state_predictor=train_config.train.detach_state_predictor,
share_graph_model=policy_config.model_predictive_rl.
share_graph_model)
elif policy_config.name == 'gat_predictive_rl':
trainer = MPRLTrainer(
model,
policy.state_predictor,
memory,
device,
policy,
writer,
batch_size,
optimizer,
env.human_num,
reduce_sp_update_frequency=train_config.train.
reduce_sp_update_frequency,
freeze_state_predictor=train_config.train.freeze_state_predictor,
detach_state_predictor=train_config.train.detach_state_predictor,
share_graph_model=policy_config.model_predictive_rl.
share_graph_model)
elif policy_config.name == 'td3_rl':
policy.set_action(action_dim, max_action, min_action)
trainer = TD3RLTrainer(
policy.actor,
policy.critic,
policy.state_predictor,
memory,
device,
policy,
writer,
batch_size,
optimizer,
env.human_num,
reduce_sp_update_frequency=train_config.train.
reduce_sp_update_frequency,
freeze_state_predictor=train_config.train.freeze_state_predictor,
detach_state_predictor=train_config.train.detach_state_predictor,
share_graph_model=policy_config.model_predictive_rl.
share_graph_model)
else:
trainer = VNRLTrainer(model, memory, device, policy, batch_size,
optimizer, writer)
explorer = Explorer(env,
robot,
device,
writer,
memory,
policy.gamma,
target_policy=policy)
policy.save_model(in_weight_file)
# imitation learning
# if args.resume:
# if not os.path.exists(rl_weight_file):
# logging.error('RL weights does not exist')
# policy.load_state_dict(torch.load(rl_weight_file))
# model = policy.get_model()
# 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):
# policy.load_state_dict(torch.load(rl_weight_file))
# model = policy.get_model()
# logging.info('Load imitation learning trained weights.')
# else:
# il_episodes = train_config.imitation_learning.il_episodes
# il_policy = train_config.imitation_learning.il_policy
# il_epochs = train_config.imitation_learning.il_epochs
# il_learning_rate = train_config.imitation_learning.il_learning_rate
# trainer.set_learning_rate(il_learning_rate)
# if robot.visible:
# safety_space = 0
# else:
# safety_space = train_config.imitation_learning.safety_space
# il_policy = policy_factory[il_policy]()
# il_policy.set_common_parameters(policy_config)
# 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)
# policy.save_model(il_weight_file)
# logging.info('Finish imitation learning. Weights saved.')
# logging.info('Experience set size: %d/%d', len(memory), memory.capacity)
trainer.update_target_model(model)
# reinforcement learning
policy.set_env(env)
robot.set_policy(policy)
robot.print_info()
trainer.set_rl_learning_rate(rl_learning_rate)
# fill the memory pool with some RL experience
if args.resume:
robot.policy.set_epsilon(epsilon_end)
explorer.run_k_episodes(100, 'train', update_memory=True, episode=0)
logging.info('Experience set size: %d/%d', len(memory),
memory.capacity)
episode = 0
best_val_reward = -1
best_val_return = -1
best_val_model = None
# evaluate the model after imitation learning
if episode % evaluation_interval == 0:
logging.info(
'Evaluate the model instantly after imitation learning on the validation cases'
)
explorer.run_k_episodes(env.case_size['val'], 'val', episode=episode)
explorer.log('val', episode // evaluation_interval)
if args.test_after_every_eval:
explorer.run_k_episodes(env.case_size['test'],
'test',
episode=episode,
print_failure=True)
explorer.log('test', episode // evaluation_interval)
episode = 0
reward_rec = []
return_rec = []
discom_tim_rec = []
nav_time_rec = []
total_time_rec = []
reward_in_last_interval = 0
return_in_last_interval = 0
nav_time__in_last_interval = 0
discom_time_in_last_interval = 0
total_time_in_last_interval = 0
eps_count = 0
fw = open(sys_args.output_dir + '/data.txt', 'w')
print("%f %f %f %f %f" % (0, 0, 0, 0, 0), file=fw)
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
_, _, nav_time, sum_reward, ave_return, discom_time, total_time = \
explorer.run_k_episodes(sample_episodes, 'train', update_memory=True, episode=episode)
eps_count = eps_count + 1
reward_in_last_interval = reward_in_last_interval + sum_reward
return_in_last_interval = return_in_last_interval + ave_return
nav_time__in_last_interval = nav_time__in_last_interval + nav_time
discom_time_in_last_interval = discom_time_in_last_interval + discom_time
total_time_in_last_interval = total_time_in_last_interval + total_time
interval = 100
if eps_count % interval == 0:
reward_rec.append(reward_in_last_interval / 100.0)
return_rec.append(return_in_last_interval / 100.0)
discom_tim_rec.append(discom_time_in_last_interval / 100.0)
nav_time_rec.append(nav_time__in_last_interval / 100.0)
total_time_rec.append(total_time_in_last_interval / 100.0)
logging.info(
'Train in episode %d reward in last 100 episodes %f %f %f %f %f',
eps_count, reward_rec[-1], return_rec[-1], discom_tim_rec[-1],
nav_time_rec[-1], total_time_rec[-1])
print("%f %f %f %f %f" %
(reward_rec[-1], return_rec[-1], discom_tim_rec[-1],
nav_time_rec[-1], total_time_rec[-1]),
file=fw)
reward_in_last_interval = 0
return_in_last_interval = 0
nav_time__in_last_interval = 0
discom_time_in_last_interval = 0
total_time_in_last_interval = 0
min_reward = (np.min(reward_rec) // 5.0) * 5.0
max_reward = (np.max(reward_rec) // 5.0 + 1) * 5.0
pos = np.array(range(1, len(reward_rec) + 1)) * interval
plt.plot(pos,
reward_rec,
color='r',
marker='.',
linestyle='dashed')
plt.axis([0, eps_count, min_reward, max_reward])
savefig(args.output_dir + "/reward_record.jpg")
explorer.log('train', episode)
trainer.optimize_batch(train_batches, episode)
episode += 1
if episode % target_update_interval == 0:
trainer.update_target_model(model)
# evaluate the model
if episode % evaluation_interval == 0:
_, _, _, reward, average_return, _, _ = explorer.run_k_episodes(
env.case_size['val'], 'val', episode=episode)
explorer.log('val', episode // evaluation_interval)
if episode % checkpoint_interval == 0 and average_return > best_val_return:
best_val_return = average_return
best_val_model = copy.deepcopy(policy.get_state_dict())
# test after every evaluation to check how the generalization performance evolves
if args.test_after_every_eval:
explorer.run_k_episodes(env.case_size['test'],
'test',
episode=episode,
print_failure=True)
explorer.log('test', episode // evaluation_interval)
if episode != 0 and episode % checkpoint_interval == 0:
current_checkpoint = episode // checkpoint_interval - 1
save_every_checkpoint_rl_weight_file = rl_weight_file.split(
'.')[0] + '_' + str(current_checkpoint) + '.pth'
policy.save_model(save_every_checkpoint_rl_weight_file)
# # test with the best val model
fw.close()
if best_val_model is not None:
policy.load_state_dict(best_val_model)
torch.save(best_val_model, os.path.join(args.output_dir,
'best_val.pth'))
logging.info('Save the best val model with the return: {}'.format(
best_val_return))
explorer.run_k_episodes(env.case_size['test'],
'test',
episode=episode,
print_failure=True)