def train():
args = parse_a2c_args()
args2 = parse_a2c_args()
output_dir = initialize_logging(args)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
num_updates = int(
args.num_frames) // args.num_steps // args.num_environments
# Create the train and test environments with Multiple processes
train_envs = MultiEnv(args.simulator,
args.num_environments,
args,
is_train=True)
#Création des environnements de test des niveaux classiques
args2.scenario_dir = "scenarios_transfer_learning/mazes_classic_test/"
args2.scenario = "custom_scenario_test{:003}.cfg"
classic_test_envs = MultiEnv(args.simulator,
args.num_environments,
args2,
is_train=False)
#Création des environnements de test des niveaux peignes
args2.scenario_dir = "scenarios_transfer_learning/little_combs_test/"
little_combs_test_envs = MultiEnv(args.simulator,
args.num_environments,
args2,
is_train=False)
args2.scenario_dir = "scenarios_transfer_learning/medium_combs_test/"
medium_combs_test_envs = MultiEnv(args.simulator,
args.num_environments,
args2,
is_train=False)
test_envs = MultiEnv(args.simulator,
args.num_environments,
args,
is_train=False)
# Writer will output to ./runs/ directory by default
writer = torch.utils.tensorboard.SummaryWriter()
obs_shape = train_envs.obs_shape
# The agent's policy network and training algorithm A2C
policy = CNNPolicy(obs_shape, args).to(device)
agent = A2CAgent(policy,
args.hidden_size,
value_weight=args.value_loss_coef,
entropy_weight=args.entropy_coef,
num_steps=args.num_steps,
num_parallel=args.num_environments,
gamma=args.gamma,
lr=args.learning_rate,
opt_alpha=args.alpha,
opt_momentum=args.momentum,
max_grad_norm=args.max_grad_norm)
start_j = 0
if args.reload_model:
checkpoint_idx = args.reload_model.split(',')[1]
checkpoint_filename = '{}models/base_line.pth.tar'.format(output_dir)
agent.load_model(checkpoint_filename)
start_j = 0 #(int(checkpoint_idx) // args.num_steps // args.num_environments) + 1
obs = train_envs.reset()
start = time.time()
nb_of_saves = 0
for j in range(start_j, num_updates):
print("------", j / num_updates * 100, "-------")
# Test des performances du modèle
if not args.skip_eval and j % args.eval_freq == 0:
total_num_steps = (j + 1) * args.num_environments * args.num_steps
mean_rewards_classic, game_times_classic = agent.evaluate(
classic_test_envs, j, total_num_steps)
mean_rewards_little, game_times_little = agent.evaluate(
little_combs_test_envs, j, total_num_steps)
mean_rewards_medium, game_times_medium = agent.evaluate(
medium_combs_test_envs, j, total_num_steps)
# succes_classic = sum([1 if i!=525 else 0 for i in game_times_classic])/16
# succes_little = sum([1 if i!=525 else 0 for i in game_times_little])/16
# succes_medium = sum([1 if i!=525 else 0 for i in game_times_medium])/16
writer.add_scalar("Reward classic levels", mean_rewards_classic, j)
writer.add_scalar("Reward little combs levels",
mean_rewards_little, j)
writer.add_scalar("Reward medium combs levels",
mean_rewards_medium, j)
# writer.add_scalar("Success rate classic levels", succes_classic, j)
# writer.add_scalar("Success rate little combs levels", succes_little, j)
# writer.add_scalar("Success rate medium combs levels", succes_medium, j)
for step in range(args.num_steps):
action = agent.get_action(obs, step)
obs, reward, done, info = train_envs.step(action)
agent.add_rewards_masks(reward, done, step)
report = agent.update(obs)
if j % args.log_interval == 0:
end = time.time()
total_num_steps = (j + 1) * args.num_environments * args.num_steps
save_num_steps = (start_j) * args.num_environments * args.num_steps
FPS = int((total_num_steps - save_num_steps) / (end - start)),
logging.info(report.format(j, total_num_steps, FPS))
if j % args.model_save_rate == 0:
nb_of_saves += 1
agent.save_policy2(nb_of_saves, args, output_dir)
# cancel the env processes
train_envs.cancel()
test_envs.cancel()
def train():
args = parse_a2c_args()
output_dir = initialize_logging(args)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
num_updates = int(args.num_frames) // args.num_steps // args.num_environments
# Create the train and test environments with Multiple processes
train_envs = MultiEnv(args.simulator, args.num_environments, args, is_train=True)
test_envs = MultiEnv(args.simulator, args.num_environments, args, is_train=False)
obs_shape = train_envs.obs_shape
# The agent's policy network and training algorithm A2C
policy = CNNPolicy(obs_shape, args).to(device)
agent = A2CAgent(policy,
args.hidden_size,
value_weight=args.value_loss_coef,
entropy_weight=args.entropy_coef,
num_steps=args.num_steps,
num_parallel=args.num_environments,
gamma=args.gamma,
lr=args.learning_rate,
opt_alpha=args.alpha,
opt_momentum=args.momentum,
max_grad_norm=args.max_grad_norm)
start_j = 0
if args.reload_model:
checkpoint_idx = args.reload_model.split(',')[1]
checkpoint_filename = '{}models/checkpoint_{}.pth.tar'.format(output_dir, checkpoint_idx)
agent.load_model(checkpoint_filename)
start_j = (int(checkpoint_idx) // args.num_steps // args.num_environments) + 1
obs = train_envs.reset()
start = time.time()
for j in range(start_j, num_updates):
if not args.skip_eval and j % args.eval_freq == 0:
total_num_steps = (j + 1) * args.num_environments * args.num_steps
mean_rewards, game_times = agent.evaluate(test_envs, j, total_num_steps)
logging.info(mean_rewards)
logging.info(game_times)
for step in range(args.num_steps):
action = agent.get_action(obs, step)
obs, reward, done, info = train_envs.step(action)
agent.add_rewards_masks(reward, done, step)
report = agent.update(obs)
if j % args.log_interval == 0:
end = time.time()
total_num_steps = (j + 1) * args.num_environments * args.num_steps
save_num_steps = (start_j) * args.num_environments * args.num_steps
FPS = int((total_num_steps - save_num_steps) / (end - start)),
logging.info(report.format(j, total_num_steps, FPS))
if j % args.model_save_rate == 0:
total_num_steps = (j + 1) * args.num_environments * args.num_steps
agent.save_policy(total_num_steps, args, output_dir)
# cancel the env processes
train_envs.cancel()
test_envs.cancel()
