def load_checkpoint(file_dir, i_epoch, layer_sizes, input_size, device='cuda'):
checkpoint = torch.load(os.path.join(file_dir, "ckpt_eps%d.pt" % i_epoch),
map_location=device)
policy_net = PolicyNet(layer_sizes).to(device)
value_net = ValueNet(input_size).to(device)
policy_net.load_state_dict(checkpoint["policy_net"])
policy_net.train()
value_net.load_state_dict(checkpoint["value_net"])
value_net.train()
policy_lr = checkpoint["policy_lr"]
valuenet_lr = checkpoint["valuenet_lr"]
policynet_optim = optim.Adam(policy_net.parameters(), lr=policy_lr)
policynet_optim.load_state_dict(checkpoint["policynet_optim"])
valuenet_optim = optim.Adam(value_net.parameters(), lr=valuenet_lr)
valuenet_optim.load_state_dict(checkpoint["valuenet_optim"])
checkpoint.pop("policy_net")
checkpoint.pop("value_net")
checkpoint.pop("policynet_optim")
checkpoint.pop("valuenet_optim")
checkpoint.pop("i_epoch")
checkpoint.pop("policy_lr")
checkpoint.pop("valuenet_lr")
return policy_net, value_net, policynet_optim, valuenet_optim, checkpoint
class A3CLocal:
def __init__(self, config):
self.config = config
# 리플레이메모리
self.replay_memory = deque(maxlen=self.config.n_replay_memory)
# 정책신경망 생성
self.actor = PolicyNet(self.config.n_state, self.config.n_action)
self.actor.to(device)
# 가치신경망 생성
self.critic = ValueNet(self.config.n_state, 1)
self.critic.to(device)
# 정책신경망의 출력을 받아 확률적으로 행동을 선택
def get_action(self, state):
state = torch.tensor(state, dtype=torch.float).to(device)
policy = self.actor(state)
policy = policy.detach().cpu().numpy()[0]
return np.random.choice(self.config.n_action, 1, p=policy)[0]
# 리플에이메모리 추가
def append_replay(self, state, action, reward, next_state):
act = np.zeros(self.config.n_action)
act[action] = 1
self.replay_memory.append((state, act, reward, next_state))
# 리플에이메모리 조회 및 클리어
def get_replay(self):
# 히스토리를 배열 형태로 정렬
replay_memory = np.array(self.replay_memory)
self.replay_memory.clear()
states = np.vstack(replay_memory[:, 0])
actions = list(replay_memory[:, 1])
rewards = list(replay_memory[:, 2])
next_states = list(replay_memory[:, 3])
return states, actions, rewards, next_states
# 글로벌신병망의 wegith를 로컬신경망으로 복사
def update_local_model(self, actor_dict, critic_dict):
self.actor.load_state_dict(actor_dict)
self.critic.load_state_dict(critic_dict)
# GPU 메모리 반납
def close(self):
del self.actor
del self.critic
def main():
#Parse arguments
#----------------------------
parser = argparse.ArgumentParser()
parser.add_argument("--env", default="CartPole-v0")
parser.add_argument("--conti", action="store_true")
parser.add_argument("--unwrap", action="store_true")
args = parser.parse_args()
#Parameters
#----------------------------
n_env = 8
n_step = 128
mb_size = n_env * n_step
sample_mb_size = 64
sample_n_epoch = 4
clip_val = 0.2
lamb = 0.95
gamma = 0.99
ent_weight = 0.0
max_grad_norm = 0.5
beta = 0.1
lr = 1e-4
n_iter = 30000
disp_step = 30
save_step = 300
save_dir = "./save"
device = "cuda:0"
expert_path = "../save/{}_traj.pkl".format(args.env)
#Create multiple environments
#----------------------------
env = MultiEnv([
make_env(i,
env_id=args.env,
unwrap=args.unwrap,
rand_seed=int(time.time())) for i in range(n_env)
])
if args.conti:
s_dim = env.ob_space.shape[0]
a_dim = env.ac_space.shape[0]
else:
s_dim = env.ob_space.shape[0]
a_dim = env.ac_space.n
runner = EnvRunner(env,
s_dim,
a_dim,
n_step,
gamma,
lamb,
device=device,
conti=args.conti)
#Load expert trajectories
#----------------------------
if os.path.exists(expert_path):
s_real, a_real = pkl.load(open(expert_path, "rb"))
sa_real = []
if args.conti:
for i in range(len(s_real)):
sa_real.append(np.concatenate([s_real[i], a_real[i]], 1))
else:
for i in range(len(s_real)):
a_real_onehot = np.zeros((len(a_real[i]), a_dim),
dtype=np.float32)
for j in range(len(a_real[i])):
a_real_onehot[j, a_real[i][j]] = 1
sa_real.append(np.concatenate([s_real[i], a_real_onehot], 1))
sa_real = np.concatenate(sa_real, 0)
else:
print("ERROR: No expert trajectory file found")
sys.exit(1)
#Create model
#----------------------------
policy_net = PolicyNet(s_dim, a_dim, conti=args.conti).to(device)
value_net = ValueNet(s_dim).to(device)
dis_net = DiscriminatorNet(s_dim + a_dim).to(device)
agent = PPO(policy_net,
value_net,
dis_net,
a_dim,
beta,
lr,
max_grad_norm,
ent_weight,
clip_val,
sample_n_epoch,
sample_mb_size,
mb_size,
device=device,
conti=args.conti)
#Load model
#----------------------------
if not os.path.exists(save_dir):
os.mkdir(save_dir)
if os.path.exists(os.path.join(save_dir, "{}.pt".format(args.env))):
print("Loading the model ... ", end="")
checkpoint = torch.load(
os.path.join(save_dir, "{}.pt".format(args.env)))
policy_net.load_state_dict(checkpoint["PolicyNet"])
value_net.load_state_dict(checkpoint["ValueNet"])
dis_net.load_state_dict(checkpoint["DiscriminatorNet"])
agent.beta = checkpoint["beta"]
start_it = checkpoint["it"]
print("Done.")
else:
start_it = 0
#Start training
#----------------------------
t_start = time.time()
policy_net.train()
value_net.train()
for it in range(start_it, n_iter):
#Run the environment
with torch.no_grad():
mb_obs, mb_actions, mb_old_a_logps, mb_values, mb_returns = runner.run(
policy_net, value_net, dis_net)
mb_advs = mb_returns - mb_values
mb_advs = (mb_advs - mb_advs.mean()) / (mb_advs.std() + 1e-6)
#Train
pg_loss, v_loss, ent, dis_loss, dis_real, dis_fake, avg_kl = agent.train(
policy_net, value_net, dis_net, mb_obs, mb_actions, mb_values,
mb_advs, mb_returns, mb_old_a_logps, sa_real)
#Print the result
if it % disp_step == 0:
agent.lr_decay(it, n_iter)
policy_net.eval()
value_net.eval()
n_sec = time.time() - t_start
fps = int((it - start_it) * n_env * n_step / n_sec)
mean_true_return, std_true_return, mean_return, std_return, mean_len = runner.get_performance(
)
policy_net.train()
value_net.train()
print("[{:5d} / {:5d}]".format(it, n_iter))
print("----------------------------------")
print("Timesteps = {:d}".format((it - start_it) * mb_size))
print("Elapsed time = {:.2f} sec".format(n_sec))
print("FPS = {:d}".format(fps))
print("actor loss = {:.6f}".format(pg_loss))
print("critic loss = {:.6f}".format(v_loss))
print("dis loss = {:.6f}".format(dis_loss))
print("entropy = {:.6f}".format(ent))
print("avg_kl = {:.6f}".format(avg_kl))
print("beta = {:.6f}".format(agent.beta))
print("mean true return = {:.6f}".format(mean_true_return))
print("mean return = {:.6f}".format(mean_return))
print("mean length = {:.2f}".format(mean_len))
print("dis_real = {:.3f}".format(dis_real))
print("dis_fake = {:.3f}".format(dis_fake))
print()
#Save model
if it % save_step == 0:
print("Saving the model ... ", end="")
torch.save(
{
"beta": agent.beta,
"it": it,
"PolicyNet": policy_net.state_dict(),
"ValueNet": value_net.state_dict(),
"DiscriminatorNet": dis_net.state_dict()
}, os.path.join(save_dir, "{}.pt".format(args.env)))
print("Done.")
print()
env.close()
def main():
#Parse arguments
#----------------------------
parser = argparse.ArgumentParser()
parser.add_argument("--env", default="CartPole-v0")
parser.add_argument("--conti", action="store_true")
parser.add_argument("--unwrap", action="store_true")
args = parser.parse_args()
#Parameters
#----------------------------
n_env = 8
n_step = 128
mb_size = n_env * n_step
sample_mb_size = 64
sample_n_epoch = 4
clip_val = 0.2
lamb = 0.95
gamma = 0.99
ent_weight = 0.0
max_grad_norm = 0.5
lr = 1e-4
n_iter = 30000
disp_step = 30
save_step = 300
save_dir = "./save"
device = "cuda:0"
#Create multiple environments
#----------------------------
env = MultiEnv([
make_env(i,
env_id=args.env,
unwrap=args.unwrap,
rand_seed=int(time.time())) for i in range(n_env)
])
if args.conti:
s_dim = env.ob_space.shape[0]
a_dim = env.ac_space.shape[0]
else:
s_dim = env.ob_space.shape[0]
a_dim = env.ac_space.n
runner = EnvRunner(env,
s_dim,
a_dim,
n_step,
gamma,
lamb,
device=device,
conti=args.conti)
#Create model
#----------------------------
policy_net = PolicyNet(s_dim, a_dim, conti=args.conti).to(device)
value_net = ValueNet(s_dim).to(device)
agent = PPO(policy_net,
value_net,
lr,
max_grad_norm,
ent_weight,
clip_val,
sample_n_epoch,
sample_mb_size,
mb_size,
device=device)
#Load model
#----------------------------
if not os.path.exists(save_dir):
os.mkdir(save_dir)
if os.path.exists(os.path.join(save_dir, "{}.pt".format(args.env))):
print("Loading the model ... ", end="")
checkpoint = torch.load(
os.path.join(save_dir, "{}.pt".format(args.env)))
policy_net.load_state_dict(checkpoint["PolicyNet"])
value_net.load_state_dict(checkpoint["ValueNet"])
start_it = checkpoint["it"]
print("Done.")
else:
start_it = 0
#Start training
#----------------------------
t_start = time.time()
policy_net.train()
value_net.train()
for it in range(start_it, n_iter):
#Run the environment
with torch.no_grad():
mb_obs, mb_actions, mb_old_a_logps, mb_values, mb_returns = runner.run(
policy_net, value_net)
mb_advs = mb_returns - mb_values
mb_advs = (mb_advs - mb_advs.mean()) / (mb_advs.std() + 1e-6)
#Train
pg_loss, v_loss, ent = agent.train(policy_net, value_net, mb_obs,
mb_actions, mb_values, mb_advs,
mb_returns, mb_old_a_logps)
#Print the result
if it % disp_step == 0:
agent.lr_decay(it, n_iter)
policy_net.eval()
value_net.eval()
n_sec = time.time() - t_start
fps = int((it - start_it) * n_env * n_step / n_sec)
mean_return, std_return, mean_len = runner.get_performance()
policy_net.train()
value_net.train()
print("[{:5d} / {:5d}]".format(it, n_iter))
print("----------------------------------")
print("Timesteps = {:d}".format((it - start_it) * mb_size))
print("Elapsed time = {:.2f} sec".format(n_sec))
print("FPS = {:d}".format(fps))
print("actor loss = {:.6f}".format(pg_loss))
print("critic loss = {:.6f}".format(v_loss))
print("entropy = {:.6f}".format(ent))
print("mean return = {:.6f}".format(mean_return))
print("mean length = {:.2f}".format(mean_len))
print()
#Save model
if it % save_step == 0:
print("Saving the model ... ", end="")
torch.save(
{
"it": it,
"PolicyNet": policy_net.state_dict(),
"ValueNet": value_net.state_dict()
}, os.path.join(save_dir, "{}.pt".format(args.env)))
print("Done.")
print()
env.close()
class A2CAgent:
def __init__(self, config):
self.config = config
# replay memory
self.replay_memory = deque(maxlen=self.config.n_replay_memory)
# 정책신경망 생성
self.actor = PolicyNet(self.config.n_state, self.config.n_action)
self.actor.to(device)
self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=self.config.actor_lr)
# 가치신경망 생성
self.critic = ValueNet(self.config.n_state, 1)
self.critic.to(device)
self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=self.config.critic_lr)
# 정책신경망의 출력을 받아 확률적으로 행동을 선택
def get_action(self, state):
state = torch.tensor(state, dtype=torch.float).to(device)
policy = self.actor(state)
policy = policy.detach().cpu().numpy()[0]
return np.random.choice(self.config.n_action, 1, p=policy)[0]
# 히스토리 추가
def append_replay(self, state, action, reward, next_state):
act = np.zeros(self.config.n_action)
act[action] = 1
self.replay_memory.append((state, act, reward, next_state))
# 리턴값 계산
def get_returns(self, rewards, done, next_value):
returns = torch.zeros(len(rewards), dtype=torch.float).to(self.config.device)
R = 0 if done else next_value
for i in reversed(range(0, len(rewards))):
R = rewards[i] + self.config.discount_factor * R
returns[i] = R
return returns
# 각 타임스텝마다 정책신경망과 가치신경망을 업데이트
def train_model(self, done):
# 히스토리를 배열 형태로 정렬
replay_memory = np.array(self.replay_memory)
self.replay_memory.clear()
states = np.vstack(replay_memory[:, 0])
actions = list(replay_memory[:, 1])
rewards = list(replay_memory[:, 2])
next_states = list(replay_memory[:, 3])
states = torch.tensor(states, dtype=torch.float).to(self.config.device)
actions = torch.tensor(actions, dtype=torch.float).to(self.config.device)
next_states = torch.tensor(next_states, dtype=torch.float).to(self.config.device)
next_values = self.critic(next_states).view(-1)
# 리턴값 계산
returns = self.get_returns(rewards, done, next_values[-1])
values = self.critic(states).view(-1)
# 가치신경망 학습
critic_loss = self.train_critic(values, returns)
# 정책신경망 학습
actor_loss = self.train_actor(states, actions, returns - values)
return actor_loss, critic_loss
# 정책신경망을 업데이트하는 함수
def train_actor(self, states, actions, advantages):
policy = self.actor(states)
action_prob = torch.sum(actions * policy, dim=1)
cross_entropy = torch.log(action_prob + 1.e-7) * advantages.detach()
actor_loss = -torch.mean(cross_entropy)
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
return actor_loss.item()
# 가치신경망을 업데이트하는 states
def train_critic(self, values, targets):
critic_loss = torch.mean(torch.pow(targets - values, 2))
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
return critic_loss.item()
# model의 weight를 파일로 저장
def save(self):
torch.save(self.actor.state_dict(), self.config.save_file + ".actor")
torch.save(self.critic.state_dict(), self.config.save_file + ".critic")
# 파일로 부터 model의 weight를 읽어 옴
def load(self):
self.actor.load_state_dict(torch.load(self.config.save_file + ".actor"))
self.critic.load_state_dict(torch.load(self.config.save_file + ".critic"))
# GPU 메모리 반납
def close(self):
del self.actor
del self.critic
def main():
#Parse arguments
#----------------------------
parser = argparse.ArgumentParser()
parser.add_argument("--env", default="BipedalWalker-v3")
parser.add_argument("--discrete", action="store_true")
parser.add_argument("--unwrap", action="store_true")
args = parser.parse_args()
#Parameters
#----------------------------
clip_val = 0.2
sample_mb_size = 64
sample_n_epoch = 4
lamb = 0.95
gamma = 0.99
ent_weight = 0.01
max_grad_norm = 0.5
lr = 1e-4
n_iter = 10000
disp_step = 30
save_step = 300
save_dir = "./save"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
#Create environment
#----------------------------
env = gym.make(args.env)
if args.discrete:
s_dim = env.observation_space.shape[0]
a_dim = env.action_space.n
else:
s_dim = env.observation_space.shape[0]
a_dim = env.action_space.shape[0]
if args.unwrap:
env = env.unwrapped
runner = EnvRunner(s_dim,
a_dim,
gamma,
lamb,
max_step=2048,
device=device,
conti=not args.discrete)
#Create model
#----------------------------
policy_net = PolicyNet(s_dim, a_dim, conti=not args.discrete).to(device)
value_net = ValueNet(s_dim).to(device)
agent = PPO(policy_net,
value_net,
lr,
max_grad_norm,
ent_weight,
clip_val,
sample_n_epoch,
sample_mb_size,
device=device)
#Load model
#----------------------------
if not os.path.exists(save_dir):
os.mkdir(save_dir)
if os.path.exists(os.path.join(save_dir, "{}.pt".format(args.env))):
print("Loading the model ... ", end="")
checkpoint = torch.load(
os.path.join(save_dir, "{}.pt".format(args.env)))
policy_net.load_state_dict(checkpoint["PolicyNet"])
value_net.load_state_dict(checkpoint["ValueNet"])
start_it = checkpoint["it"]
print("Done.")
else:
start_it = 0
#Start training
#----------------------------
t_start = time.time()
policy_net.train()
value_net.train()
mean_total_reward = 0
mean_length = 0
for it in range(start_it, n_iter):
#Run the environment
with torch.no_grad():
mb_obs, mb_actions, mb_old_a_logps, mb_values, mb_returns, mb_rewards = runner.run(
env, policy_net, value_net)
mb_advs = mb_returns - mb_values
mb_advs = (mb_advs - mb_advs.mean()) / (mb_advs.std() + 1e-6)
#Train
pg_loss, v_loss, ent = agent.train(policy_net, value_net, mb_obs,
mb_actions, mb_values, mb_advs,
mb_returns, mb_old_a_logps)
mean_total_reward += mb_rewards.sum()
mean_length += len(mb_obs)
print("[Episode {:4d}] total reward = {:.6f}, length = {:d}".format(
it, mb_rewards.sum(), len(mb_obs)))
#Print the result
if it % disp_step == 0:
print("\n[{:5d} / {:5d}]".format(it, n_iter))
print("----------------------------------")
print("Elapsed time = {:.2f} sec".format(time.time() - t_start))
print("actor loss = {:.6f}".format(pg_loss))
print("critic loss = {:.6f}".format(v_loss))
print("entropy = {:.6f}".format(ent))
print("mean return = {:.6f}".format(mean_total_reward /
disp_step))
print("mean length = {:.2f}".format(mean_length / disp_step))
print()
agent.lr_decay(it, n_iter)
mean_total_reward = 0
mean_length = 0
#Save model
if it % save_step == 0:
print("Saving the model ... ", end="")
torch.save(
{
"it": it,
"PolicyNet": policy_net.state_dict(),
"ValueNet": value_net.state_dict()
}, os.path.join(save_dir, "{}.pt".format(args.env)))
print("Done.")
print()
env.close()
class DQNAgent:
def __init__(self, config):
self.config = config
self.epsilon = config.epsilon
# replay memory
self.replay_memory = deque(maxlen=self.config.n_replay_memory)
# 가치신경망 생성
self.model = ValueNet(self.config.n_state, self.config.n_action)
self.model.to(device)
self.model_optimizer = torch.optim.Adam(self.model.parameters(),
lr=self.config.learning_rate)
# 정책신경망의 출력을 받아 확률적으로 행동을 선택
def get_action(self, state):
if np.random.rand() <= self.epsilon:
return random.randrange(self.config.n_action)
else:
state = torch.tensor(state,
dtype=torch.float).to(self.config.device)
output = self.model(state)
return output.argmax().item()
# 히스토리 추가
def append_replay(self, state, action, reward, next_state, done):
self.replay_memory.append((state, action, reward, next_state, done))
# 각 타임스텝마다 정책신경망과 가치신경망을 업데이트
def train_model(self):
# 학습이 계속 될 수 록 탐험 학률을 줄여 줌
if self.epsilon > self.config.epsilon_min:
self.epsilon *= self.config.epsilon_decay
# 히스토리를 배열 형태로 정렬
replay_memory = np.array(
random.sample(self.replay_memory, self.config.n_batch))
states = np.vstack(replay_memory[:, 0])
actions = list(replay_memory[:, 1])
rewards = list(replay_memory[:, 2])
next_states = list(replay_memory[:, 3])
dones = list(replay_memory[:, 4])
states = torch.tensor(states, dtype=torch.float).to(device)
next_states = torch.tensor(next_states, dtype=torch.float).to(device)
targets = self.model(states)
next_values = self.model(next_states)
for i in range(len(targets)):
if dones[i]:
targets[i][actions[i]] = rewards[i] # Vt = Rt+1
else:
targets[i][
actions[i]] = rewards[i] + self.config.discount_factor * (
torch.max(next_values[i])) # Vt = Rt+1 + rVt+1
loss = self.train_value(states, targets)
return loss
# 가치신경망을 업데이트하는 함수
def train_value(self, states, targets):
values = self.model(states)
loss = torch.mean(torch.pow(targets - values, 2))
self.model_optimizer.zero_grad()
loss.backward()
self.model_optimizer.step()
return loss.item()
# model의 weight를 파일로 저장
def save(self):
torch.save(self.model.state_dict(), self.config.save_file)
# 파일로 부터 model의 weight를 읽어 옴
def load(self):
self.model.load_state_dict(torch.load(self.config.save_file))
# GPU 메모리 반납
def close(self):
del self.model
class Qlearning:
def __init__(self):
self.value_net = ValueNet(2)
self.target_value_net = ValueNet(2)
# self.value_net.load_state_dict(torch.load('./value_net.pkl'))
torch.save(self.value_net.state_dict(), './value_net.pkl')
self.target_value_net.load_state_dict(torch.load('./value_net.pkl'))
self.episode = 0
self.explore = 0.3
self.buffer = []
self.buffer_capacity = 20
self.buffer_index = 0
def value(self, board):
board_np = np.array(board, dtype=np.float32)
board_flat = board_np.flatten()
board_tensor = torch.from_numpy(board_flat)
values_tensor = self.value_net(board_tensor.detach())
return values_tensor
def target_value(self, board):
board_np = np.array(board, dtype=np.float32)
board_flat = board_np.flatten()
board_tensor = torch.from_numpy(board_flat)
target_values_tensor = self.target_value_net(board_tensor.detach())
return target_values_tensor
def action(self, board):
epsilon = random.random()
if self.explore <= 0.9:
self.explore *= 1.001
print('explore = %.3f' % self.explore)
if epsilon > self.explore:
action_num = random.randint(0, 3)
return action_num
board_np = np.array(board, dtype=np.float32)
board_flat = board_np.flatten()
board_tensor = torch.from_numpy(board_flat)
values_tensor = self.value(board_tensor.detach())
values_np = values_tensor.detach().numpy()
max_idx = np.argmax(values_np.tolist())
return max_idx
def train(self):
# randomly select samples as one batch
train_data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
sample_size = 10 #self.buffer_capacity
indices = np.random.choice(range(10), sample_size)
sampler1 = torch.utils.data.SubsetRandomSampler(indices)
print('sampler1 = ', sampler1)
exit()
value_loss = self.value_net.loss_function(v, v_.detach(), reward)
self.value_net.zero_grad()
value_loss[idx].backward()
self.value_net.opt_Adam.step()
def update(self):
self.episode += 1
print('episode = ', self.episode)
if self.episode % 2 == 0:
torch.save(self.value_net.state_dict(), './value_net.pkl')
self.target_value_net.load_state_dict(
torch.load('./value_net.pkl'))