def main():
env = gym.make(env_name)
env.seed(500)
torch.manual_seed(500)
num_inputs = env.observation_space.shape[0]
num_actions = env.action_space.n
print('state size:', num_inputs)
print('action size:', num_actions)
net = QNet(num_inputs, num_actions)
optimizer = optim.Adam(net.parameters(), lr=lr)
writer = SummaryWriter('logs')
net.to(device)
net.train()
running_score = 0
steps = 0
loss = 0
for e in range(3000):
done = False
memory = Memory()
score = 0
state = env.reset()
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
while not done:
steps += 1
action = net.get_action(state)
next_state, reward, done, _ = env.step(action)
next_state = torch.Tensor(next_state)
next_state = next_state.unsqueeze(0)
mask = 0 if done else 1
reward = reward if not done or score == 499 else -1
action_one_hot = torch.zeros(2)
action_one_hot[action] = 1
memory.push(state, next_state, action_one_hot, reward, mask)
score += reward
state = next_state
loss = QNet.train_model(net, memory.sample(), optimizer)
score = score if score == 500.0 else score + 1
running_score = 0.99 * running_score + 0.01 * score
if e % log_interval == 0:
print('{} episode | score: {:.2f}'.format(e, running_score))
writer.add_scalar('log/score', float(running_score), e)
writer.add_scalar('log/loss', float(loss), e)
if running_score > goal_score:
break
class Agent():
def __init__(self, state_size, action_size, seed):
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.qnetwork_local = QNet(state_size, action_size, seed).to(device)
self.qnetwork_target = QNet(state_size, action_size, seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Replay Memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
self.memory.add(state, action, reward, next_state, done)
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0.1):
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_local.train()
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma):
states, actions, rewards, next_states, dones = experiences
# For normal DQN
#Q_targets_next = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(1)
# For double DQN
Q_targets_next = np.argmax(self.qnetwork_local(next_states).detach(),axis=-1).unsqueeze(1)
Q_targets_next = self.qnetwork_target(next_states).gather(1, Q_targets_next)
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
Q_expected = self.qnetwork_local(states).gather(1, actions)
loss = F.mse_loss(Q_expected, Q_targets)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
for target_param, local_param in zip(target_model.parameters(), local_model.parameters()):
target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data)
def main():
env = gym.make(env_name)
env.seed(500)
torch.manual_seed(500)
num_inputs = env.observation_space.shape[0]
num_actions = env.action_space.n
print('state size:', num_inputs)
print('action size:', num_actions)
online_net = QNet(num_inputs, num_actions)
target_net = QNet(num_inputs, num_actions)
target_net.load_state_dict(online_net.state_dict())
online_net.share_memory()
target_net.share_memory()
optimizer = SharedAdam(online_net.parameters(), lr=lr)
global_ep, global_ep_r, res_queue = mp.Value('i',
0), mp.Value('d',
0.), mp.Queue()
writer = SummaryWriter('logs')
online_net.to(device)
target_net.to(device)
online_net.train()
target_net.train()
workers = [
Worker(online_net, target_net, optimizer, global_ep, global_ep_r,
res_queue, i) for i in range(mp.cpu_count())
]
[w.start() for w in workers]
res = []
while True:
r = res_queue.get()
if r is not None:
res.append(r)
[ep, ep_r, loss] = r
writer.add_scalar('log/score', float(ep_r), ep)
writer.add_scalar('log/loss', float(loss), ep)
else:
break
[w.join() for w in workers]
def main():
env = gym.make(args.env_name)
env.seed(500)
torch.manual_seed(500)
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
print('state size:', state_size)
print('action size:', action_size)
q_net = QNet(state_size, action_size, args)
target_q_net = QNet(state_size, action_size, args)
optimizer = optim.Adam(q_net.parameters(), lr=0.001)
update_target_model(q_net, target_q_net)
writer = SummaryWriter(args.logdir)
replay_buffer = deque(maxlen=10000)
running_score = 0
steps = 0
for episode in range(args.max_iter_num):
done = False
score = 0
state = env.reset()
state = np.reshape(state, [1, state_size])
while not done:
if args.render:
env.render()
steps += 1
q_values = q_net(torch.Tensor(state))
action = get_action(q_values, action_size, args.epsilon)
next_state, reward, done, _ = env.step(action)
next_state = np.reshape(next_state, [1, state_size])
reward = reward if not done or score == 499 else -1
mask = 0 if done else 1
replay_buffer.append((state, action, reward, next_state, mask))
state = next_state
score += reward
if steps > args.initial_exploration:
args.epsilon -= args.epsilon_decay
args.epsilon = max(args.epsilon, 0.1)
mini_batch = random.sample(replay_buffer, args.batch_size)
q_net.train(), target_q_net.train()
train_model(q_net, target_q_net, optimizer, mini_batch)
if steps % args.update_target == 0:
update_target_model(q_net, target_q_net)
score = score if score == 500.0 else score + 1
running_score = 0.99 * running_score + 0.01 * score
if episode % args.log_interval == 0:
print(
'{} episode | running_score: {:.2f} | epsilon: {:.2f}'.format(
episode, running_score, args.epsilon))
writer.add_scalar('log/score', float(score), episode)
if running_score > args.goal_score:
if not os.path.isdir(args.save_path):
os.makedirs(args.save_path)
ckpt_path = args.save_path + 'model.pth.tar'
torch.save(q_net.state_dict(), ckpt_path)
print('Running score exceeds 400. So end')
break
videoResolution=[800, 600])
env.seed(500)
torch.manual_seed(500)
render_map = False
num_inputs = env.observation_space.shape
num_actions = len(env.action_names[0])
print('state size:', num_inputs)
print('action size:', num_actions)
model = QNet(num_actions)
model.apply(weights_init)
target_model = QNet(num_actions)
update_target_model(model, target_model)
model.train()
target_model.train()
optimizer = optim.Adam(model.parameters(),
lr=hp.lr,
weight_decay=hp.l2_rate)
memory = Memory(100000)
if render_map:
root, canvas = init_map()
steps = 0
scores = []
epsilon = 1.0
for episode in range(hp.num_episodes):
state = env.reset()
def main():
if not (os.path.isdir("logs")):
os.makedirs("logs")
if (args.entropy and args.boltzmann):
raise ValueError("Entropy as well as Boltzmann set.")
print(args)
working_dir = "logs/" + args.dir
if not (os.path.isdir(working_dir)):
os.mkdir(working_dir)
env = QubeSwingupEnv(use_simulator=True)
num_inputs = env.observation_space.shape[0]
num_actions = NUMBER_OF_ACTIONS
print('state size:', num_inputs)
print('action size:', num_actions)
online_net = QNet(num_inputs, num_actions)
target_net = QNet(num_inputs, num_actions)
update_target_model(online_net, target_net)
optimizer = optim.Adam(online_net.parameters(), lr=lr)
writer = SummaryWriter(working_dir)
online_net.to(device)
target_net.to(device)
online_net.train()
target_net.train()
memory = Memory_With_TDError(replay_memory_capacity)
running_score = 0
epsilon = 1.0
steps = 0
beta = beta_start
loss = 0
training_started = False
best_running_score = -1000
for e in range(args.e):
done = False
score = 0
state = env.reset()
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
start_time = time.time()
while not done:
steps += 1
action = get_action(state,
target_net,
epsilon,
use_entropy=args.entropy,
use_boltzmann=args.boltzmann)
next_state, reward, done, info = env.step(
get_continuous_action(action))
reward = give_me_reward(info["alpha"], info["theta"])
next_state = torch.Tensor(next_state).to(device)
next_state = next_state.unsqueeze(0)
mask = 0 if done else 1
action_one_hot = np.zeros(NUMBER_OF_ACTIONS)
action_one_hot[action] = 1
memory.push(state, next_state, action_one_hot, reward, mask)
score += reward
state = next_state
if steps > initial_exploration:
if not training_started:
print("---------------- training started ---------------")
training_started = True
epsilon -= 0.000005
epsilon = max(epsilon, 0.1)
beta += 0.000005
beta = min(1, beta)
batch, weights = memory.sample(batch_size, online_net,
target_net, beta)
loss = QNet.train_model(online_net, target_net, optimizer,
batch, weights, device)
if steps % update_target == 0:
update_target_model(online_net, target_net)
end_time = time.time()
running_score = 0.99 * running_score + 0.01 * score
if e % log_interval == 0:
print(
'{} episode | score: {:.2f} | epsilon: {:.2f} | beta: {:.2f}'.
format(e, running_score, epsilon, beta))
writer.add_scalar('log/score', float(running_score), e)
writer.add_scalar('log/loss', float(loss), e)
if running_score > best_running_score and args.save:
torch.save(online_net.state_dict(),
working_dir + "/best_model.pth")
best_running_score = running_score
def main():
# cartpole test
if (cartpole_test):
envs_fun = [lambda: gym.make('CartPole-v0')]
envs_fun = np.tile(envs_fun, 3)
envs = ShmemVecEnv(envs_fun)
dummy_env = envs_fun[0]()
else:
INPUT_FILE = '../data/05f2a901.json'
with open(INPUT_FILE, 'r') as f:
puzzle = json.load(f)
envs_fun = [
lambda: gym.make('arc-v0',
input=task['input'],
output=task['output'],
need_ui=need_ui) for task in puzzle['train']
]
#pdb.set_trace()
envs_fun = envs_fun[0:1]
envs = ShmemVecEnv(envs_fun)
dummy_env = envs_fun[0]()
env_num = len(envs_fun)
torch.manual_seed(500)
num_inputs = dummy_env.observation_space.shape[0]
num_actions = dummy_env.action_space.n
print('state size:', num_inputs)
print('action size:', num_actions)
online_net = QNet(num_inputs, num_actions, cartpole_test, evalution_mode)
target_net = QNet(num_inputs, num_actions, cartpole_test, evalution_mode)
if (evalution_mode):
online_net = torch.load('../result/arc0.model')
target_net = torch.load('../result/arc0.model')
update_target_model(online_net, target_net)
optimizer = optim.Adam(online_net.parameters(), lr=lr)
writer = SummaryWriter('logs')
online_net.to(device)
target_net.to(device)
online_net.train()
target_net.train()
memory = Memory(replay_memory_capacity)
score = 0
epsilon = 1.0
steps = 0
loss = 0
states = envs.reset()
try:
while True:
if (need_ui):
envs.render()
steps += 1
global initial_exploration
if (initial_exploration > 0):
initial_exploration -= 1
actions = []
for state in states:
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
action = get_action(state, target_net,
0 if evalution_mode else epsilon,
dummy_env)
if (evalution_mode):
print(action)
actions.append(action)
next_states, rewards, dones, info = envs.step(actions)
#print(rewards)
masks = np.zeros(envs.num_envs)
for i in range(envs.num_envs):
masks[i] = 0 if dones[i] else 1
for i in range(envs.num_envs):
#print(rewards[i])
action_one_hot = np.zeros(dummy_env.action_space.n)
action_one_hot[actions[i]] = 1
memory.push(states[i], next_states[i], action_one_hot,
rewards[i], masks[i])
#score += reward
states = next_states
if not evalution_mode and steps > initial_exploration:
epsilon -= 0.00003
epsilon = max(epsilon, 0.1)
batch = memory.sample(batch_size)
loss = QNet.train_model(online_net, target_net, optimizer,
batch, device)
if steps % update_target == 0:
update_target_model(online_net, target_net)
if (steps > 1028):
states = envs.reset()
steps = 0
print(
'new epsisode ------------------------------------------')
except KeyboardInterrupt:
print('save model')
torch.save(target_net, '../result/arc.model')
sys.exit(0)
def main():
env = gym.make(env_name)
env.seed(500)
torch.manual_seed(500)
### NNのIn-Outは環境によって異なる
num_inputs = env.observation_space.shape[0]
num_actions = env.action_space.n
print('state size:', num_inputs)
print('action size:', num_actions)
### 2つのNWを作成・初期化
online_net = QNet(num_inputs, num_actions)
target_net = QNet(num_inputs, num_actions)
update_target_model(online_net, target_net)
optimizer = optim.Adam(online_net.parameters(), lr=lr)
### 各NWの設定 CPU / GPU
online_net.to(device)
target_net.to(device)
### 各NWの設定 初めは学習モードにする
online_net.train()
target_net.train()
### 学習前の初期設定
memory = Memory(replay_memory_capacity)
running_score = 0
epsilon = 1.0
steps = 0
loss = 0
steps_before = 0
for e in range(3000):
done = False
score = 0
state = env.reset()
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
while not done:
steps += 1
### 行動の決定はtarget_netで行う
action = get_action(state, target_net, epsilon, env)
### 次の状態の観測、報酬の獲得
next_state, reward, done, _ = env.step(action)
next_state = torch.Tensor(next_state)
next_state = next_state.unsqueeze(0)
if e % 10 == 0:
print(next_state, action, reward)
### わかりにくいので書き変えた
if done:
mask = 0
else:
mask = 1
### memoryに記録
action_one_hot = np.zeros(num_actions)
action_one_hot[action] = 1
memory.push(state, next_state, action_one_hot, reward, mask)
### rewardは基本的に-1
score += reward ### そのepisodeで何ステップ行ったかを記録するためだけのもの
state = next_state
if steps > initial_exploration:
epsilon -= 0.00005
epsilon = max(epsilon, 0.1)
### online_net の学習
batch = memory.sample(batch_size)
loss = QNet.train_model(online_net, target_net, optimizer,
batch)
### たまにtarget_netをonline_netで上書きする
if steps % update_target == 0:
update_target_model(online_net, target_net)
print("Ep {0:04d}: {1} step".format(e, steps - steps_before))
steps_before = steps
score = score if score == 200.0 else score + 1
running_score = 0.99 * running_score + 0.01 * score
if e % log_interval == 0:
print('{} episode | score: {:.2f} | epsilon: {:.2f}'.format(
e, running_score, epsilon))
if running_score > goal_score:
break
class Agent():
"""Agent definition for interacting with environment"""
def __init__(self, state_size, action_size, seed):
"""
Params
======
state_size (int): state dimension
action_size (int): action dimension
seed (int): random seed for replicating experiment
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.QNet_local = QNet(state_size, action_size, seed).to(device)
self.QNet_target = QNet(state_size, action_size, seed).to(device)
self.optimizer = optim.Adam(self.QNet_local.parameters(), lr=LR)
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Add current experience to replay memory
self.memory.add(state, action, reward, next_state, done)
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
"""Get favored action
Params
======
state (array_like): current state
eps (float): epsilon, for epsilon-greedy action selection
"""
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.QNet_local.eval()
with torch.no_grad():
action_values = self.QNet_local(state)
self.QNet_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, experiences, gamma):
"""Perform learning on experiences
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
Q_targets_next = self.QNet_target(next_states).detach().max(
1)[0].unsqueeze(1)
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
Q_expected = self.QNet_local(states).gather(1, actions)
loss = F.mse_loss(Q_expected, Q_targets)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.soft_update(self.QNet_local, self.QNet_target, TAU)
def soft_update(self, local_model, target_model, tau):
""" θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): model to copy weights from
target_model (PyTorch model): copy to
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
def main():
env = gym.make(env_name)
env.seed(500)
torch.manual_seed(500)
num_inputs = env.observation_space.shape[0]
num_actions = env.action_space.n
print('state size:', num_inputs)
print('action size:', num_actions)
### ポリシーネットワークの構築
### inputに対してπ(a|s) と V(s) が出力される
### Vの出力は1つ が学習時にはAdvantage関数を計算する
net = QNet(num_inputs, num_actions)
optimizer = optim.Adam(net.parameters(), lr=lr)
net.to(device)
net.train()
### もろもろの初期化
running_score = 0
steps = 0
loss = 0
steps_before = 0
df = pd.DataFrame(index=range(10000),
columns=["steps", "loss_policy", "loss_value"])
memory = Memory()
for e in range(10000):
done = False
### 1エピソード分のメモリすら持たずに1ステップずつ学習
### 環境を初期状態に
score = 0
state = env.reset()
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
while not done:
steps += 1
### epsilon は使わず、各行動の評価値を確率に直接変換して行動を決定
action = net.get_action(state)
next_state, reward, done, _ = env.step(action)
next_state = torch.Tensor(next_state)
next_state = next_state.unsqueeze(0)
mask = 0 if done else 1
reward = reward if not done or score == 499 else -1
action_one_hot = torch.zeros(num_actions)
action_one_hot[action] = 1
transition = [state, next_state, action, reward, mask]
memory.push(state, next_state, action_one_hot, reward, mask)
score += reward
state = next_state
steps_before = steps
score = score if score == 500.0 else score + 1
running_score = 0.99 * running_score + 0.01 * score
if e % 16 == 0:
### 16ステップごとに、まとめて学習
loss, loss_policy, loss_value = QNet.train_model(
net, optimizer, memory.sample())
### メモリの初期化
memory = Memory()
df.loc[e, "steps"] = running_score
df.loc[e, "loss_policy"] = loss_policy
df.loc[e, "loss_value"] = loss_value
print(
"Ep {0:04d}: score: {1:02d}, loss_policy: {2}, loss_value: {3}"
.format(e, int(running_score), loss_policy, loss_value))
if running_score > goal_score:
break
df.to_csv("loss.csv")
def train(render):
online_net = QNet(h=84, w=84, outputs=36)
online_net.load_state_dict(torch.load('saved/online_net.pt'))
target_net = QNet(h=84, w=84, outputs=36)
update_target_model(online_net, target_net)
optimizer = optim.Adam(online_net.parameters(), lr=lr)
online_net.to(device)
target_net.to(device)
online_net.train()
target_net.train()
memory = Memory(replay_memory_capacity)
memory = torch.load('saved/model_memory.pt')
epsilon = 0.1
steps = 0
beta = beta_start
loss = 0
for e in range(100000):
#level = random.choice(LEVEL_SET)
level = 'Level01'
env = make_retro(game=env_name,
state=level,
use_restricted_actions=retro.Actions.DISCRETE)
done = False
total_reward = 0.0
state = env.reset()
state = torch.Tensor(state).to(device).permute(2, 0, 1)
#state = state.view(state.size()[0], -1)
state = state.unsqueeze(0)
while not done:
steps += 1
action = get_action(state.to(device), target_net, epsilon, env)
if render:
env.render()
next_state, reward, done, info = env.step(action)
next_state = torch.Tensor(next_state).permute(2, 0, 1)
#next_state = next_state.view(next_state.size()[0], -1)
next_state = next_state.unsqueeze(0)
total_reward += reward
mask = 0 if done else 1
action_one_hot = torch.zeros(36)
action_one_hot[action] = 1
reward = torch.tensor([info['score']]).to(device)
memory.push(state, next_state, action_one_hot, reward, mask)
state = next_state
if len(memory) > initial_exploration:
epsilon -= 0.00005
epsilon = max(epsilon, 0.02)
beta += 0.00005
beta = min(1, beta)
batch, weights = memory.sample(batch_size, online_net,
target_net, beta)
loss = QNet.train_model(online_net, target_net, optimizer,
batch, weights)
if steps % update_target == 0:
update_target_model(online_net, target_net)
if e % 1 == 0:
print('{} episode | Total Reward: {}'.format(e, total_reward))
torch.save(online_net.state_dict(), 'saved/online_net.pt')
torch.save(memory, 'saved/model_memory.pt')
env.close()
def main():
env = gym.make(env_name)
env.seed(500)
torch.manual_seed(500)
num_inputs = 2
num_actions = env.action_space.n
print('state size:', num_inputs)
print('action size:', num_actions)
online_net = QNet(num_inputs, num_actions)
target_net = QNet(num_inputs, num_actions)
update_target_model(online_net, target_net)
optimizer = optim.Adam(online_net.parameters(), lr=lr)
writer = SummaryWriter('logs')
online_net.to(device)
target_net.to(device)
online_net.train()
target_net.train()
memory = Memory(replay_memory_capacity)
running_score = 0
epsilon = 1.0
steps = 0
loss = 0
for e in range(30000):
done = False
state_series = deque(maxlen=sequence_length)
next_state_series = deque(maxlen=sequence_length)
score = 0
state = env.reset()
state = state_to_partial_observability(state)
state = torch.Tensor(state).to(device)
next_state_series.append(state)
while not done:
steps += 1
state_series.append(state)
action = get_action(state_series, target_net, epsilon, env)
next_state, reward, done, _ = env.step(action)
next_state = state_to_partial_observability(next_state)
next_state = torch.Tensor(next_state)
mask = 0 if done else 1
reward = reward if not done or score == 499 else -1
action_one_hot = np.zeros(2)
action_one_hot[action] = 1
if len(state_series) >= sequence_length:
memory.push(state_series, next_state_series, action_one_hot,
reward, mask)
score += reward
state = next_state
if steps > initial_exploration:
epsilon -= 0.000005
epsilon = max(epsilon, 0.1)
batch = memory.sample(batch_size)
loss = QNet.train_model(online_net, target_net, optimizer,
batch)
if steps % update_target == 0:
update_target_model(online_net, target_net)
score = score if score == 500.0 else score + 1
if running_score == 0:
running_score = score
else:
running_score = 0.99 * running_score + 0.01 * score
if e % log_interval == 0:
print('{} episode | score: {:.2f} | epsilon: {:.2f}'.format(
e, running_score, epsilon))
writer.add_scalar('log/score', float(running_score), e)
writer.add_scalar('log/loss', float(loss), e)
if running_score > goal_score:
break
def main():
env = gym.make(args.env_name)
env.seed(500)
torch.manual_seed(500)
img_shape = env.observation_space.shape
num_actions = 3
print('image size:', img_shape)
print('action size:', num_actions)
net = QNet(num_actions)
target_net = QNet(num_actions)
update_target_model(net, target_net)
optimizer = optim.RMSprop(net.parameters(), lr=0.00025, eps=0.01)
writer = SummaryWriter('logs')
if not os.path.isdir(args.save_path):
os.makedirs(args.save_path)
net.to(device)
target_net.to(device)
net.train()
target_net.train()
memory = Memory(100000)
running_score = 0
epsilon = 1.0
steps = 0
for e in range(10000):
done = False
dead = False
score = 0
avg_loss = []
start_life = 5
state = env.reset()
state = pre_process(state)
state = torch.Tensor(state).to(device)
history = torch.stack((state, state, state, state))
for i in range(3):
action = env.action_space.sample()
state, reward, done, info = env.step(action)
state = pre_process(state)
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
history = torch.cat((state, history[:-1]), dim=0)
while not done:
if args.render:
env.render()
steps += 1
qvalue = net(history.unsqueeze(0))
action = get_action(epsilon, qvalue, num_actions)
next_state, reward, done, info = env.step(action + 1)
next_state = pre_process(next_state)
next_state = torch.Tensor(next_state).to(device)
next_state = next_state.unsqueeze(0)
next_history = torch.cat((next_state, history[:-1]), dim=0)
if start_life > info['ale.lives']:
dead = True
start_life = info['ale.lives']
score += reward
reward = np.clip(reward, -1, 1)
mask = 0 if dead else 1
memory.push(history.cpu(), next_history.cpu(), action, reward,
mask)
if dead:
dead = False
if steps > args.initial_exploration:
epsilon -= 1e-6
epsilon = max(epsilon, 0.1)
batch = memory.sample(args.batch_size)
loss = train_model(net, target_net, optimizer, batch)
if steps % args.update_target:
update_target_model(net, target_net)
else:
loss = 0
avg_loss.append(loss)
history = next_history
if e % args.log_interval == 0:
print(
'{} episode | score: {:.2f} | epsilon: {:.4f} | steps: {} | loss: {:.4f}'
.format(e, score, epsilon, steps, np.mean(avg_loss)))
writer.add_scalar('log/score', float(score), steps)
writer.add_scalar('log/score', np.mean(avg_loss), steps)
if score > args.goal_score:
ckpt_path = args.save_path + 'model.pth'
torch.save(net.state_dict(), ckpt_path)
print('running score exceeds 400 so end')
break
def main():
env = gym.make(env_name)
env.seed(500)
torch.manual_seed(500)
### NNのIn-Outは環境によって異なる
# num_inputs = env.observation_space.shape[0]
num_inputs = 1024
num_actions = env.action_space.n
print('state size:', num_inputs)
print('action size:', num_actions)
### 2つのNWを作成・初期化
online_net = QNet(num_inputs, num_actions)
target_net = QNet(num_inputs, num_actions)
update_target_model(online_net, target_net)
optimizer = optim.Adam(online_net.parameters(), lr=lr)
### 各NWの設定 CPU / GPU
online_net.to(device)
target_net.to(device)
### 各NWの設定 初めは学習モードにする
online_net.train()
target_net.train()
### 特徴抽出用の学習済みモデル
# pre_model = models.resnet50(pretrained=True)
# pre_model.fc = nn.Identity()
pre_model = models.squeezenet1_0(pretrained=True)
pre_model.classifier = nn.AdaptiveAvgPool2d((1, 1))
pre_model.to(device)
def state_to_feature(state):
state_img = render_cv2img(state[0], state[2])
state_img = cv2.resize(state_img, (224, 224))[:, :, 0]
state_img = state_img.reshape((1, 224, 224))
state_img_rgb = np.zeros((1, 3, 224, 224))
state_img_rgb[:, 0] = state_img
state_img_rgb[:, 1] = state_img
state_img_rgb[:, 2] = state_img
state_img_rgb_tensor = torch.Tensor(state_img_rgb).to(device)
state_feature = pre_model(state_img_rgb_tensor)
return state_feature
### メモリの保存場所（改修中）
memory_dir = "memory/"
memory = Memory(replay_memory_capacity, memory_dir)
### 学習前の初期設定
running_score = 0
epsilon = 1.0
steps = 0
loss = 0
steps_before = 0
for e in range(3000):
done = False
score = 0
### state = [位置, 速度, 角度, 角速度]
state = env.reset(
) ### [-0.01517264 0.02423424 0.02480018 -0.04009749]
### state = [[2048次元のベクトル]]
state = state_to_feature(state)
### 前の時間の情報が無いときついため、それを入れるためのもの 最初はstateと同値でよさそう
previous_state = state
while not done:
steps += 1
### 行動の決定はtarget_netで行う
previous_present_state = torch.cat((previous_state, state), 1)
action = get_action(previous_present_state, target_net, epsilon,
env)
### 次の状態の観測、報酬の獲得
next_state, reward, done, _ = env.step(action)
next_state = state_to_feature(next_state)
present_next_state = torch.cat((state, next_state), 1)
### わかりにくいので書き変えた
if done:
mask = 0
else:
mask = 1
if (done and (score != 499)): ### 499ステップまで行かずにdoneになったら
reward = -1
else:
pass ### rewardは基本的に1
### memoryに記録
action_one_hot = np.zeros(2)
action_one_hot[action] = 1
memory.push(previous_present_state, present_next_state,
action_one_hot, reward, mask)
### rewardは基本的に1
score += reward ### そのepisodeで何ステップ行ったかを記録するためだけのもの
if steps > initial_exploration:
epsilon -= 0.00005
epsilon = max(epsilon, 0.1)
### online_net の学習
batch = memory.sample(batch_size)
loss = QNet.train_model(online_net, target_net, optimizer,
batch)
### たまにtarget_netをonline_netで上書きする
if steps % update_target == 0:
update_target_model(online_net, target_net)
### 次のステップ
previous_state = state
state = next_state
print("Ep {0:04d}: {1} step".format(e, steps - steps_before))
steps_before = steps
score = score if score == 500.0 else score + 1
running_score = 0.99 * running_score + 0.01 * score
if e % log_interval == 0:
print('{} episode | score: {:.2f} | epsilon: {:.2f}'.format(
e, running_score, epsilon))
if running_score > goal_score:
break
def main():
### 環境を初期化
env = gym.make(env_name)
env.seed(500)
torch.manual_seed(500)
num_inputs = env.observation_space.shape[0]
num_actions = env.action_space.n
print('state size:', num_inputs)
print('action size:', num_actions)
### ポリシーネットワークの構築
net = QNet(num_inputs, num_actions)
optimizer = optim.Adam(net.parameters(), lr=lr)
net.to(device)
net.train()
### もろもろの初期化
running_score = 0
steps = 0
loss = 0
steps_before = 0
for e in range(10000):
done = False
### 1エピソードごとにMemoryは空にする（実質、Experience Replay がない）
memory = Memory()
### 環境を初期状態に
score = 0
state = env.reset()
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
while not done:
steps += 1
### epsilon は使わず、各行動の評価値を確率に直接変換して行動を決定
action = net.get_action(state)
next_state, reward, done, _ = env.step(action)
next_state = torch.Tensor(next_state)
next_state = next_state.unsqueeze(0)
mask = 0 if done else 1
reward = reward if not done or score == 499 else -1
action_one_hot = torch.zeros(num_actions)
action_one_hot[action] = 1
memory.push(state, next_state, action_one_hot, reward, mask)
score += reward
state = next_state
### 1エピソード分をまとめて学習
### memory.sample はランダムに選択ではなく、1エピソードのmemory全体を返す
loss = QNet.train_model(net, optimizer, memory.sample())
print("Ep {0:04d}: {1} step".format(e, steps - steps_before))
steps_before = steps
score = score if score == 500.0 else score + 1
running_score = 0.99 * running_score + 0.01 * score
if e % log_interval == 0:
print('{} episode | score: {:.2f}'.format(e, running_score))
if running_score > goal_score:
break
class Agent():
def __init__(self, args, state_size, action_size, seed):
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.per = args.per
self.dueling = args.dueling
self.buffer_size = args.buffer_size
self.batch_size = args.batch_size
self.gamma = args.gamma
self.tau = args.tau
self.lr = args.learning_rate
self.update_freq = args.update_every
# Q-Network
if self.dueling:
self.local_qnet = DuelingQNet(state_size, action_size,
seed).to(device)
self.target_qnet = DuelingQNet(state_size, action_size,
seed).to(device)
else:
self.local_qnet = QNet(state_size, action_size, seed).to(device)
self.target_qnet = QNet(state_size, action_size, seed).to(device)
self.optimizer = optim.Adam(self.local_qnet.parameters(), lr=self.lr)
# Replay Memory
if self.per:
self.memory = PrioritizedReplayMemory(args, self.buffer_size)
else:
self.memory = ReplayMemory(action_size, self.buffer_size,
self.batch_size, seed)
self.t_step = 0 # init time step for updating every UPDATE_EVERY steps
def step(self, state, action, reward, next_state, done):
if self.per:
self.memory.append(state, action, reward, next_state, done)
else:
self.memory.add(state, action, reward, next_state,
done) # save experience to replay memory.
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % self.update_freq
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > self.batch_size:
if self.dueling:
self.learn_DDQN(self.gamma)
else:
self.learn(self.gamma)
def act(self, state, eps=0.):
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
self.local_qnet.eval()
with torch.no_grad():
action_values = self.local_qnet(state)
self.local_qnet.train()
# Epsilon-greedy action selection
if random.random() > eps:
return np.argmax(action_values.cpu().data.numpy())
else:
return random.choice(np.arange(self.action_size))
def learn(self, gamma):
if self.per:
idxs, states, actions, rewards, next_states, dones, weights = self.memory.sample(
self.batch_size)
else:
states, actions, rewards, next_states, dones = self.memory.sample()
# Get max predicted Q values for next states from target model
Q_targets_next = self.target_qnet(next_states).detach().max(
1)[0].unsqueeze(1)
# Compute Q targets for current states
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
Q_expected = self.local_qnet(states).gather(1, actions)
# Compute loss - element-wise mean squared error
# Now loss is a Tensor of shape (1,)
# loss.item() gets the scalar value held in the loss.
loss = F.mse_loss(Q_expected, Q_targets)
# Minimize loss
self.optimizer.zero_grad()
if self.per:
(weights * loss).mean().backward(
) # Backpropagate importance-weighted minibatch loss
else:
loss.backward()
self.optimizer.step()
if self.per:
errors = np.abs((Q_expected - Q_targets).detach().cpu().numpy())
self.memory.update_priorities(idxs, errors)
# Update target network
self.soft_update(self.local_qnet, self.target_qnet, self.tau)
def learn_DDQN(self, gamma):
if self.per:
idxs, states, actions, rewards, next_states, dones, weights = self.memory.sample(
self.batch_size)
else:
states, actions, rewards, next_states, dones = self.memory.sample()
# Get index of maximum value for next state from Q_expected
Q_argmax = self.local_qnet(next_states).detach()
_, a_prime = Q_argmax.max(1)
# Get max predicted Q values for next states from target model
Q_targets_next = self.target_qnet(next_states).detach().gather(
1, a_prime.unsqueeze(1))
# Compute Q targets for current states
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Get expected Q values from local model
Q_expected = self.local_qnet(states).gather(1, actions)
# Compute loss
# Now loss is a Tensor of shape (1,)
# loss.item() gets the scalar value held in the loss.
loss = F.mse_loss(Q_expected, Q_targets)
# Minimize loss
self.optimizer.zero_grad()
if self.per:
(weights * loss).mean().backward(
) # Backpropagate importance-weighted minibatch loss
else:
loss.backward()
self.optimizer.step()
if self.per:
errors = np.abs((Q_expected - Q_targets).detach().cpu().numpy())
self.memory.update_priorities(idxs, errors)
# Update target network
self.soft_update(self.local_qnet, self.target_qnet, self.tau)
def soft_update(self, local_model, target_model, tau):
# θ_target = τ*θ_local + (1 - τ)*θ_target
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
def main():
env = gym.make(args.env_name)
env.seed(500)
torch.manual_seed(500)
num_inputs = env.observation_space.shape[0]
num_actions = env.action_space.n
print('state size:', num_inputs)
print('action size:', num_actions)
net = QNet(num_inputs, num_actions)
target_net = QNet(num_inputs, num_actions)
update_target_model(net, target_net)
optimizer = optim.Adam(net.parameters(), lr=0.001)
writer = SummaryWriter('logs')
if not os.path.isdir(args.save_path):
os.makedirs(args.save_path)
net.to(device)
target_net.to(device)
net.train()
target_net.train()
memory = Memory(10000)
running_score = 0
epsilon = 1.0
steps = 0
for e in range(3000):
done = False
score = 0
state = env.reset()
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
while not done:
if args.render:
env.render()
steps += 1
qvalue = net(state)
action = get_action(epsilon, qvalue, num_actions)
next_state, reward, done, _ = env.step(action)
next_state = torch.Tensor(next_state)
next_state = next_state.unsqueeze(0)
mask = 0 if done else 1
reward = reward if not done or score == 499 else -1
memory.push(state, next_state, action, reward, mask)
score += reward
state = next_state
if steps > args.initial_exploration:
epsilon -= 0.00005
epsilon = max(epsilon, 0.1)
batch = memory.sample(args.batch_size)
train_model(net, target_net, optimizer, batch, args.batch_size)
if steps % args.update_target:
update_target_model(net, target_net)
score = score if score == 500.0 else score + 1
running_score = 0.99 * running_score + 0.01 * score
if e % args.log_interval == 0:
print('{} episode | score: {:.2f} | epsilon: {:.2f}'.format(
e, running_score, epsilon))
writer.add_scalar('log/score', float(score), running_score)
if running_score > args.goal_score:
ckpt_path = args.save_path + 'model.pth'
torch.save(net.state_dict(), ckpt_path)
print('running score exceeds 400 so end')
break
def main():
env = gym.make(env_name)
env.seed(500)
torch.manual_seed(500)
num_inputs = env.observation_space.shape[0]
num_actions = env.action_space.n
print('state size:', num_inputs)
print('action size:', num_actions)
### ポリシーネットワークの構築
### inputに対してπ(a|s) と Q(s, a) が出力される
### 次元とユニット数は2つで同じ
net = QNet(num_inputs, num_actions)
optimizer = optim.Adam(net.parameters(), lr=lr)
net.to(device)
net.train()
### もろもろの初期化
running_score = 0
steps = 0
loss = 0
steps_before = 0
df = pd.DataFrame(index=range(10000), columns=["steps", "loss_policy", "loss_value"])
for e in range(10000):
done = False
### 1エピソード分のメモリすら持たずに1ステップずつ学習
### 環境を初期状態に
score = 0
state = env.reset()
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
lp = []
lv = []
while not done:
steps += 1
### epsilon は使わず、各行動の評価値を確率に直接変換して行動を決定
action = net.get_action(state)
next_state, reward, done, _ = env.step(action)
next_state = torch.Tensor(next_state)
next_state = next_state.unsqueeze(0)
mask = 0 if done else 1
reward = reward if not done or score == 499 else -1
transition = [state, next_state, action, reward, mask]
score += reward
state = next_state
### 1ステップごとに、そのステップの結果のみを学習
loss, loss_policy, loss_value = QNet.train_model(net, optimizer, transition)
# loss = QNet.train_model(net, optimizer, transition)
lp.append(loss_policy.item())
lv.append(loss_value.item())
lp = np.asarray(lp[:-1]).sum() / (len(lp) - 1)
lv = np.asarray(lv[:-1]).sum() / (len(lv) - 1)
print("Ep {0:04d}: {1} step, loss_policy: {2}, loss_value: {3}".format(e, steps - steps_before, lp, lv))
# print("Ep {0:04d}: {1} step".format(e, steps - steps_before))
df.loc[e, "steps"] = steps - steps_before
df.loc[e, "loss_policy"] = lp
df.loc[e, "loss_value"] = lv
steps_before = steps
score = score if score == 500.0 else score + 1
running_score = 0.99 * running_score + 0.01 * score
if e % log_interval == 0:
print('{} episode | score: {:.2f}'.format(e, running_score))
if running_score > goal_score:
break
df.to_csv("loss.csv")
def main():
env = gym.make(env_name)
env.seed(500)
torch.manual_seed(500)
num_inputs = env.observation_space.shape[0]
num_actions = env.action_space.n
print('state size:', num_inputs)
print('action size:', num_actions)
online_net = QNet(num_inputs, num_actions)
target_net = QNet(num_inputs, num_actions)
update_target_model(online_net, target_net)
optimizer = optim.Adam(online_net.parameters(), lr=lr)
writer = SummaryWriter('logs')
online_net.to(device)
target_net.to(device)
online_net.train()
target_net.train()
memory = Memory_With_TDError(replay_memory_capacity)
running_score = 0
epsilon = 1.0
steps = 0
beta = beta_start
loss = 0
for e in range(3000):
done = False
score = 0
state = env.reset()
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
while not done:
steps += 1
action = get_action(state, target_net, epsilon, env)
next_state, reward, done, _ = env.step(action)
next_state = torch.Tensor(next_state)
next_state = next_state.unsqueeze(0)
mask = 0 if done else 1
reward = reward if not done or score == 499 else -1
action_one_hot = np.zeros(2)
action_one_hot[action] = 1
memory.push(state, next_state, action_one_hot, reward, mask)
score += reward
state = next_state
if steps > initial_exploration:
epsilon -= 0.00005
epsilon = max(epsilon, 0.1)
beta += 0.00005
beta = min(1, beta)
batch, weights = memory.sample(batch_size, online_net,
target_net, beta)
loss = QNet.train_model(online_net, target_net, optimizer,
batch, weights)
if steps % update_target == 0:
update_target_model(online_net, target_net)
score = score if score == 500.0 else score + 1
running_score = 0.99 * running_score + 0.01 * score
if e % log_interval == 0:
print(
'{} episode | score: {:.2f} | epsilon: {:.2f} | beta: {:.2f}'.
format(e, running_score, epsilon, beta))
writer.add_scalar('log/score', float(running_score), e)
writer.add_scalar('log/loss', float(loss), e)
if running_score > goal_score:
break
def main(L, mouse_initial_indices, rewardlist, actions_list):
if mouse_initial_indices is None:
all_possible_starting_positions = np.array([*np.where(L == 1)]).T
scores = [0]
best_scores = [0]
env = deepcopy(L)
torch.manual_seed(2020)
num_inputs = 2 + 1
num_actions = 4
print('state size:', num_inputs)
print('action size:', num_actions)
online_net = QNet(num_inputs, num_actions)
target_net = QNet(num_inputs, num_actions)
update_target_model(online_net, target_net)
optimizer = optim.Adam(online_net.parameters(), lr=lr)
# writer = SummaryWriter('logs')
online_net.to(device)
target_net.to(device)
online_net.train()
target_net.train()
memory = Memory(replay_memory_capacity)
running_score = 0
epsilon = 1.0
steps = 0
loss = 0
inint = mouse_initial_indices
best_score = 0
number_episode = 1000
for e in range(number_episode):
if inint is None:
mouse_initial_indices = all_possible_starting_positions[
np.random.choice(range(len(all_possible_starting_positions)))]
done = False
env = deepcopy(L)
eaubue = 0.
score = 0
state = np.array(mouse_initial_indices)
state = torch.Tensor(state).to(device)
state = state.unsqueeze(0)
while not done:
steps += 1
action = get_action(state, target_net, epsilon, env, eaubue=eaubue)
newstate = state + torch.Tensor(np.array(
actions_list[action])).to(device)
if env[int(newstate[0][0].tolist()),
int(newstate[0][1].tolist())] != 0:
next_state = newstate
new_eaubue = eaubue
reward = rewardlist[env[int(newstate[0][0].tolist()),
int(newstate[0][1].tolist())]]
if env[int(newstate[0][0].tolist()),
int(newstate[0][1].tolist())] == 2:
done = True
if env[int(newstate[0][0].tolist()),
int(newstate[0][1].tolist()
)] == 4: #if the mouse is in the water
env[int(newstate[0][0].tolist()),
int(newstate[0][1].tolist()
)] = 5 #there is no more water
new_eaubue = 1.
else:
next_state = state
reward = rewardlist[0]
new_eaubue = eaubue
mask = 0 if done else 1
action_one_hot = np.zeros(4)
action_one_hot[action] = 1
memory.push(
torch.cat((
state,
torch.tensor(eaubue).unsqueeze(0).unsqueeze(0).to(device)),
1),
torch.cat((next_state, torch.tensor(new_eaubue).unsqueeze(
0).unsqueeze(0).to(device)), 1), action_one_hot, reward,
mask)
score += reward
state = next_state
eaubue = new_eaubue
if steps > initial_exploration:
epsilon -= 0.00005
epsilon = max(epsilon, 0.1)
batch = memory.sample(batch_size)
loss = QNet.train_model(online_net, target_net, optimizer,
batch)
if steps % update_target == 0:
update_target_model(online_net, target_net)
# print("OK")
if score > 35:
print(score)
running_score = 0.99 * running_score + 0.01 * score
# running_score=score
scores.append(running_score)
best_scores.append(
score if score > best_scores[-1] else best_scores[-1])
if e % log_interval == 0:
print(
'{} episode | score: {:.2f} | best score: {:.2f} | epsilon: {:.2f}'
.format(e, running_score, best_score, epsilon))
# writer.add_scalar('log/score', float(running_score), e)
# writer.add_scalar('log/loss', float(loss), e)
if score > best_score:
best_score = score
torch.save(online_net.state_dict(), "./qlearning_model")
if running_score > goal_score:
break
return number_episode, scores, best_scores
