def train(opt):
#print('decay', opt.num_decay_epochs)
if torch.cuda.is_available():
torch.cuda.manual_seed(123)
else:
torch.manual_seed(123)
if os.path.isdir(opt.log_path):
shutil.rmtree(opt.log_path)
os.makedirs(opt.log_path)
writer = SummaryWriter(opt.log_path)
env = Tetris(width=opt.width, height=opt.height,
block_size=opt.block_size) #高さ、幅、1ブロックの大きさを指定
model = DeepQNetwork() #インスタンス生成
#optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr)
optimizer = torch.optim.SGD(model.parameters(), lr=opt.lr)
criterion = nn.MSELoss()
state = env.reset() # 初期状態 tensor([0., 0., 0., 0.])
if torch.cuda.is_available():
model.cuda()
state = state.cuda()
replay_memory = deque(maxlen=opt.replay_memory_size) #maxで30000、
epoch = 0
while epoch < opt.num_epochs: # 指定したエポック数繰り返す
#1ピース目の取りうる全ての行動に対して、それぞれ状態を計算 {(左から何番目か,何回転か):tensor([,,,]),*n}
next_steps = env.get_next_states()
# εグリーディー的なやつ
#epsilon = opt.final_epsilon + (max(opt.num_decay_epochs - epoch, 0) * ( #num_decay_epochs以降一定
# opt.initial_epsilon - opt.final_epsilon) / opt.num_decay_epochs)
epsilon = opt.initial_epsilon - opt.initial_epsilon * epoch / opt.num_epochs #直線
u = random() # 0～1
random_action = u <= epsilon # True, False
next_actions, next_states = zip(
*next_steps.items()) #next_stepsのkeyとvalueを取得 #( , )*n
next_states = torch.stack(next_states) # tensor([[ , , , ],*n])
if torch.cuda.is_available():
next_states = next_states.cuda()
model.eval()
with torch.no_grad():
predictions = model(
next_states
)[:, 0] #DeepQNetworkのforward #tensor([,～,])これはそれぞれの行動に対するQ値のようなもの
model.train()
# next_stepsのインデックスをランダムor最適で指定
if random_action: # ランダムな行動
index = randint(0, len(next_steps) - 1)
else: # 最適な行動(最大のpredictionsに基づく)
index = torch.argmax(predictions).item()
# 行動と次の状態を決定
next_state = next_states[
index, :] #ある行動を選択したときの次の状態 #tensor([ , , , ])
action = next_actions[index] #行動 #(左から何番目か,何回転か)
reward, done = env.step(
action, epoch, render=False) #行動を実行、報酬(スコア)を求める、溢れた場合done=True、描画
if torch.cuda.is_available():
next_state = next_state.cuda()
replay_memory.append(
[state, reward, next_state, done]
) #deque([[tensor([0., 0., 0., 0.]), 1, tensor([0., 0., 2., 4.]), False]],..., maxlen=30000)
if done: # 溢れた場合 or 上限100手
final_score = env.score
final_tetrominoes = env.tetrominoes
final_cleared_lines = env.cleared_lines
cleared_lines1 = env.cleared_lines1
cleared_lines2 = env.cleared_lines2
cleared_lines3 = env.cleared_lines3
cleared_lines4 = env.cleared_lines4
state = env.reset() # 初期状態 tensor([0., 0., 0., 0.])
if torch.cuda.is_available():
state = state.cuda()
else: # 溢れてない場合
state = next_state # 状態を更新 tensor([0., 1., 2., 5.])とか
continue #while epoch～に戻る
#if len(replay_memory) < opt.replay_memory_size / 1000: #溢れた場合判定(累計ピースが3000以下ならcontinue)
#continue #pass
# 累計ピースが3000に到達した後、溢れる毎に以下を実行
epoch += 1
batch = sample(
replay_memory, min(len(replay_memory), opt.batch_size)
) #replay_memoryからbatch_size個ランダムに取り出す(len(replay_memory) < opt.batch_sizeのときはlen(replay_memory)個取り出す)
replay_memory.clear() #中身を全消去
state_batch, reward_batch, next_state_batch, done_batch = zip(*batch)
state_batch = torch.stack(
tuple(state for state in
state_batch)) #tensor([[0., 26., 16., 62.],*batch_size個])
reward_batch = torch.from_numpy(
np.array(reward_batch,
dtype=np.float32)[:, None]) #tensor([[1.],*batch_size個])
next_state_batch = torch.stack(
tuple(
state for state in
next_state_batch)) #tensor([[0., 32., 13., 72.],*batch_size個])
if torch.cuda.is_available():
state_batch = state_batch.cuda()
reward_batch = reward_batch.cuda()
next_state_batch = next_state_batch.cuda()
q_values = model(
state_batch) #予測Q値、q_values=tensor([[0.1810],*batch_size個])
model.eval()
with torch.no_grad():
next_prediction_batch = model(next_state_batch) #次の状態に対する予測Q値
model.train()
# Q値の正解値を更新式で求める
y_batch = torch.cat(
tuple(reward if done else reward + opt.gamma * prediction
for reward, done, prediction in zip(
reward_batch, done_batch, next_prediction_batch)))[:,
None]
optimizer.zero_grad() #最適化アルゴリズム
loss = criterion(q_values, y_batch) #損失関数はmse、q_values:予測値、y_batch:正解値
"""
length = len(q_values)
errors = np.zeros([length])
print('size', len(q_values), len(y_batch))
for i in range(length):
print('Q', q_values[i])
print('Y', y_batch[i])
errors[i] = (q_values[i] - y_batch[i]) ** 2
error = np.mean(errors)
print('error', error)
print('loss',loss)
"""
loss.backward()
optimizer.step()
if epoch % 10 == 0:
print(
"Epoch: {}/{}, Action: {}, Score: {}, Tetrominoes {}, Cleared lines: {}"
.format(epoch, opt.num_epochs, action, final_score,
final_tetrominoes, final_cleared_lines))
#学習中のスコアをcsvに記録
if epoch == 1:
with open('Score_train.csv', mode='w',
newline="") as Score_train_Record:
writer = csv.writer(Score_train_Record)
writer.writerow([
epoch, final_tetrominoes, final_score, final_cleared_lines,
cleared_lines1, cleared_lines2, cleared_lines3,
cleared_lines4
])
else:
with open('Score_train.csv', mode='a',
newline="") as Score_train_Record:
writer = csv.writer(Score_train_Record)
writer.writerow([
epoch, final_tetrominoes, final_score, final_cleared_lines,
cleared_lines1, cleared_lines2, cleared_lines3,
cleared_lines4
])
"""
writer.add_scalar('Train/Score', final_score, epoch - 1)
writer.add_scalar('Train/Tetrominoes', final_tetrominoes, epoch - 1)
writer.add_scalar('Train/Cleared lines', final_cleared_lines, epoch - 1)
"""
if epoch > 0 and epoch % opt.save_interval == 0:
torch.save(model, "{}/tetris2_{}".format(
opt.saved_path, epoch)) #定期的にモデルをtrained_modelsに保存
if final_tetrominoes > 500: #ミノ数が500を超えたモデルの重みとバイアスをcsvに保存
save_model_parameter(model)
torch.save(model,
"{}/tetris2".format(opt.saved_path)) #学習後のモデルをtrained_modelsに保存
def train(opt):
if torch.cuda.is_available():
torch.cuda.manual_seed(123)
else:
torch.manual_seed(123)
model = DeepQNetwork()
model_target = DeepQNetwork()
if os.path.isdir(opt.log_path):
shutil.rmtree(opt.log_path)
os.makedirs(opt.log_path)
writer = SummaryWriter(opt.log_path)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-6)
criterion = nn.MSELoss()
game_state = FlappyBird()
image, reward, terminal, score = game_state.next_frame(0)
image = pre_processing(
image[:game_state.screen_width, :int(game_state.base_y)],
opt.image_size, opt.image_size)
image = torch.from_numpy(image)
if torch.cuda.is_available():
model.cuda()
model_target.cuda()
image = image.cuda()
state = torch.cat(tuple(image for _ in range(4)))[None, :, :, :]
model_target.eval()
replay_memory = []
iter = 0
while iter < opt.num_iters:
prediction = model(state)[0]
# Exploration or exploitation
epsilon = opt.final_epsilon + (
(opt.num_iters - iter) *
(opt.initial_epsilon - opt.final_epsilon) / opt.num_iters)
u = random()
random_action = u <= epsilon
if random_action:
#print("Perform a random action")
action = randint(0, 1)
else:
action = torch.argmax(prediction).item()
next_image, reward, terminal, score = game_state.next_frame(action)
next_image = pre_processing(
next_image[:game_state.screen_width, :int(game_state.base_y)],
opt.image_size, opt.image_size)
next_image = torch.from_numpy(next_image)
if torch.cuda.is_available():
next_image = next_image.cuda()
next_state = torch.cat((state[0, 1:, :, :], next_image))[None, :, :, :]
replay_memory.append([state, action, reward, next_state, terminal])
if len(replay_memory) > opt.replay_memory_size:
del replay_memory[0]
batch = sample(replay_memory, min(len(replay_memory), opt.batch_size))
state_batch, action_batch, reward_batch, next_state_batch, terminal_batch = zip(
*batch)
state_batch = torch.cat(tuple(state for state in state_batch))
action_batch = torch.from_numpy(
np.array([[1, 0] if action == 0 else [0, 1]
for action in action_batch],
dtype=np.float32))
reward_batch = torch.from_numpy(
np.array(reward_batch, dtype=np.float32)[:, None])
next_state_batch = torch.cat(tuple(state
for state in next_state_batch))
if torch.cuda.is_available():
state_batch = state_batch.cuda()
action_batch = action_batch.cuda()
reward_batch = reward_batch.cuda()
next_state_batch = next_state_batch.cuda()
current_prediction_batch = model(state_batch)
next_prediction_batch = model_target(next_state_batch)
y_batch = torch.cat(
tuple(reward if terminal else reward +
opt.gamma * prediction[max_action]
for reward, terminal, prediction, max_action in zip(
reward_batch, terminal_batch, next_prediction_batch,
torch.argmax(model(next_state_batch), axis=1))))
q_value = torch.sum(current_prediction_batch * action_batch, dim=1)
optimizer.zero_grad()
# y_batch = y_batch.detach()
loss = criterion(q_value, y_batch)
loss.backward()
optimizer.step()
state = next_state
if iter % opt.target_update_freq == 0:
model_target.load_state_dict(model.state_dict())
iter += 1
if iter % 100 == 0:
print(
"Test::Double Q: Iteration: {}/{}, Action: {}, Loss: {}, Epsilon {}, Reward: {}, Q-value: {}"
.format(iter + 1, opt.num_iters, action, loss, epsilon, reward,
torch.max(prediction)))
writer.add_scalar('Train/Loss', loss, iter)
writer.add_scalar('Train/Epsilon', epsilon, iter)
writer.add_scalar('Train/Reward', reward, iter)
writer.add_scalar('Train/Q-value', torch.max(prediction), iter)
writer.add_scalar('Train/score', score, iter)
if (iter + 1) % 1000000 == 0:
torch.save(model,
"{}/flappy_bird_{}".format(opt.saved_path, iter + 1))
torch.save(model, "{}/flappy_bird".format(opt.saved_path))
def train(opt):
if torch.cuda.is_available():
# 随机数种子seed确定时，模型的训练结果将始终保持一致
torch.cuda.manual_seed(123)
else:
torch.manual_seed(123)
if os.path.isdir(opt.log_path):
shutil.rmtree(opt.log_path)
os.makedirs(opt.log_path)
writer = SummaryWriter(opt.log_path)
env = Tetris(width=opt.width, height=opt.height, block_size=opt.block_size)
model = DeepQNetwork()
optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr)
criterion = nn.MSELoss()
state = env.reset()
if torch.cuda.is_available():
model.cuda()
state = state.cuda()
replay_memory = deque(maxlen=opt.replay_memory_size)
epoch = 0
while epoch < opt.num_epochs:
# 得到所有可能的下落方块
next_steps = env.get_next_states()
# Exploration or exploitation
epsilon = opt.final_epsilon + (
max(opt.num_decay_epochs - epoch, 0) *
(opt.initial_epsilon - opt.final_epsilon) / opt.num_decay_epochs)
u = random()
random_action = u <= epsilon
# 下一步落下的横向坐标以及旋转，以及得到下方方块的board状态
next_actions, next_states = zip(*next_steps.items())
next_states = torch.stack(next_states)
if torch.cuda.is_available():
next_states = next_states.cuda()
model.eval()
with torch.no_grad():
predictions = model(next_states)[:, 0]
model.train()
# 采取的动作
if random_action:
index = randint(0, len(next_steps) - 1)
else:
index = torch.argmax(predictions).item()
next_state = next_states[index, :]
action = next_actions[index]
reward, done = env.step(action, render=False)
if torch.cuda.is_available():
next_state = next_state.cuda()
replay_memory.append([state, reward, next_state, done])
if done:
final_score = env.score
final_tetrominoes = env.tetrominoes
final_cleared_lines = env.cleared_lines
state = env.reset()
if torch.cuda.is_available():
state = state.cuda()
else:
state = next_state
continue
if len(replay_memory) < opt.replay_memory_size / 10:
continue
epoch += 1
batch = sample(replay_memory, min(len(replay_memory), opt.batch_size))
'''
a = [2, 3, 4], b = [5, 6, 7], c = [a, b]
e, f, g = zip(*c)
e = (2, 5), f = (3, 6), g = (4, 7) 类型为tuple
'''
state_batch, reward_batch, next_state_batch, done_batch = zip(*batch)
state_batch = torch.stack(tuple(state for state in state_batch))
reward_batch = torch.from_numpy(
np.array(reward_batch, dtype=np.float32)[:, None])
next_state_batch = torch.stack(
tuple(state for state in next_state_batch))
if torch.cuda.is_available():
state_batch = state_batch.cuda()
reward_batch = reward_batch.cuda()
next_state_batch = next_state_batch.cuda()
q_values = model(state_batch)
model.eval()
# Q_target
with torch.no_grad():
next_prediction_batch = model(next_state_batch)
model.train()
y_batch = torch.cat(
tuple(reward if done else reward + opt.gamma * prediction
for reward, done, prediction in zip(
reward_batch, done_batch, next_prediction_batch)))[:,
None]
optimizer.zero_grad()
loss = criterion(q_values, y_batch)
loss.backward()
optimizer.step()
print(
"Epoch: {}/{}, Action: {}, Score: {}, Tetrominoes {}, Cleared lines: {}"
.format(epoch, opt.num_epochs, action, final_score,
final_tetrominoes, final_cleared_lines))
writer.add_scalar('Train/Score', final_score, epoch - 1)
writer.add_scalar('Train/Tetrominoes', final_tetrominoes, epoch - 1)
writer.add_scalar('Train/Cleared lines', final_cleared_lines,
epoch - 1)
if epoch > 0 and epoch % opt.save_interval == 0:
torch.save(model, "{}/tetris_{}".format(opt.saved_path, epoch))
torch.save(model, "{}/tetris".format(opt.saved_path))
def test(opt, conv1, conv2, conv3):
if torch.cuda.is_available():
torch.cuda.manual_seed(123)
else:
torch.manual_seed(123)
# if torch.cuda.is_available():
# model = torch.load("{}/tetris".format(opt.saved_path))
# else:
# model = torch.load("{}/tetris".format(opt.saved_path), map_location=lambda storage, loc: storage)
model = DeepQNetwork()
model.eval()
if False: # save weights
ii = 1
for layer in model.modules():
if isinstance(layer, nn.Linear):
if ii == 1:
weights1 = layer.weight.cpu()
weights1 = weights1.detach().numpy()
pd.DataFrame(weights1).to_csv(
'trained_models/conv{}.csv'.format(ii))
if ii == 2:
weights2 = layer.weight.cpu()
weights2 = weights2.detach().numpy()
pd.DataFrame(weights2).to_csv(
'trained_models/conv{}.csv'.format(ii))
if ii == 3:
weights3 = layer.weight.cpu()
weights3 = weights3.detach().numpy()
pd.DataFrame(weights3).to_csv(
'trained_models/conv{}.csv'.format(ii))
ii += 1
if False: # load csv weights
ii = 1
for layer in model.modules():
if isinstance(layer, nn.Linear):
with torch.no_grad():
if ii == 1:
layer.weight.data = torch.Tensor(conv1).cuda()
if ii == 2:
layer.weight.data = torch.Tensor(conv2).cuda()
if ii == 3:
layer.weight.data = torch.Tensor(conv1).cuda()
ii += 1
env = Tetris(width=opt.width, height=opt.height, block_size=opt.block_size)
env.reset()
if torch.cuda.is_available():
model.cuda()
out = cv2.VideoWriter(
opt.output, cv2.VideoWriter_fourcc(*"MJPG"), opt.fps,
(int(1.5 * opt.width * opt.block_size), opt.height * opt.block_size))
while True:
next_steps = env.get_next_states()
next_actions, next_states = zip(*next_steps.items())
next_states = torch.stack(next_states)
if torch.cuda.is_available():
next_states = next_states.cuda()
predictions = model(next_states)[:, 0]
index = torch.argmax(predictions).item()
action = next_actions[index]
result, done = env.step(action, render=True, video=out)
if done:
out.release()
return result
def train(opt):
if torch.cuda.is_available():
torch.cuda.manual_seed(123)
else:
torch.manual_seed(123)
# TensorBoard
if os.path.isdir(opt.log_path):
shutil.rmtree(opt.log_path)
os.makedirs(opt.log_path)
writer = SummaryWriter(opt.log_path)
# Modelo
CHECKPOINT_FILE = opt.saved_path + "/" + opt.checkpoint_name
if opt.load:
if os.path.isfile(CHECKPOINT_FILE):
print("--> Carregando Checkpoint '{}'.".format(CHECKPOINT_FILE))
if torch.cuda.is_available():
model = torch.load(CHECKPOINT_FILE)
else:
model = torch.load(CHECKPOINT_FILE, map_location=lambda storage, loc: storage)
print("--> Checkpoint Carregado '{}'.".format(CHECKPOINT_FILE))
else:
print("--> Checkpoint '{}' não encontrado.".format(CHECKPOINT_FILE))
model = DeepQNetwork()
else:
model = DeepQNetwork()
optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr)
criterion = nn.MSELoss()
# Environment
env = Tetris(width=opt.width, height=opt.height)
state = env.reset()
if torch.cuda.is_available():
model.cuda()
state = state.cuda()
replay_memory = deque(maxlen=opt.replay_memory_size)
epoch = 0
prev_loss = 0
# Épocas do Checkpoint
if opt.load and "_" in opt.checkpoint_name:
start_epoch = opt.checkpoint_name.split("_")[-1]
epoch = int(start_epoch)
print("Checkpoint com {} épocas.".format(epoch))
# Loop de Treino
while epoch < opt.num_epochs:
next_steps = env.get_next_states()
# Exploração ou Explotação
epsilon = opt.final_epsilon + (max(opt.num_decay_epochs - epoch, 0) * (
opt.initial_epsilon - opt.final_epsilon) / opt.num_decay_epochs)
u = random()
random_action = u <= epsilon
next_actions, next_states = zip(*next_steps.items())
next_states = torch.stack(next_states)
if torch.cuda.is_available():
next_states = next_states.cuda()
model.eval()
with torch.no_grad():
predictions = model(next_states)[:, 0]
model.train()
if random_action:
index = randint(0, len(next_steps) - 1)
else:
index = torch.argmax(predictions).item()
next_state = next_states[index, :]
action = next_actions[index]
reward, done = env.step(action, render=True)
if torch.cuda.is_available():
next_state = next_state.cuda()
replay_memory.append([state, reward, next_state, done])
if done:
final_score = env.score
final_tetrominoes = env.tetrominoes
final_cleared_lines = env.cleared_lines
state = env.reset()
if torch.cuda.is_available():
state = state.cuda()
else:
state = next_state
continue
# Replay Buffer
if len(replay_memory) < opt.replay_memory_size / 10:
print("replay_memory ", len(replay_memory))
continue
epoch += 1
batch = sample(replay_memory, min(len(replay_memory), opt.batch_size))
state_batch, reward_batch, next_state_batch, done_batch = zip(*batch)
state_batch = torch.stack(tuple(state for state in state_batch))
reward_batch = torch.from_numpy(np.array(reward_batch, dtype=np.float32)[:, None])
next_state_batch = torch.stack(tuple(state for state in next_state_batch))
# Aprendizado
if torch.cuda.is_available():
state_batch = state_batch.cuda()
reward_batch = reward_batch.cuda()
next_state_batch = next_state_batch.cuda()
q_values = model(state_batch)
model.eval()
with torch.no_grad():
next_prediction_batch = model(next_state_batch)
model.train()
y_batch = torch.cat(
tuple(reward if done else reward + opt.gamma * prediction for reward, done, prediction in
zip(reward_batch, done_batch, next_prediction_batch)))[:, None]
optimizer.zero_grad()
loss = criterion(q_values, y_batch)
loss.backward()
optimizer.step()
prev_loss = loss.item()
print("Epoch: {}/{}, Action: {}, Score: {}, Tetrominoes {}, Cleared lines: {}".format(
epoch,
opt.num_epochs,
action,
final_score,
final_tetrominoes,
final_cleared_lines))
writer.add_scalar('Train/Score', final_score, epoch - 1)
writer.add_scalar('Train/Tetrominoes', final_tetrominoes, epoch - 1)
writer.add_scalar('Train/Cleared lines', final_cleared_lines, epoch - 1)
if epoch > 0 and epoch % opt.save_interval == 0:
torch.save(model, "{}/{}_{}".format(opt.saved_path, opt.saved_name, epoch))
torch.save(model, "{}/{}".format(opt.saved_path, opt.saved_name))
def train(opt):
cv2.setUseOptimized(True)
print("cv2 is optimized =", cv2.useOptimized())
print("cuda available =", torch.cuda.is_available())
if torch.cuda.is_available():
torch.cuda.manual_seed(123)
else:
torch.manual_seed(123)
if os.path.isdir(opt.log_path):
shutil.rmtree(opt.log_path)
os.makedirs(opt.log_path)
writer = SummaryWriter(opt.log_path)
#############
# The environment in this situation is the Tetris game itself
# Create a Tetris object with the constructor in src/tetris.py and pass in the 3 arguments described there
# the arguments default values are stored in "opt" which comes from the argument parser above
#############
env = #.....
model = DeepQNetwork()
optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr)
criterion = nn.MSELoss()
state = env.reset()
if torch.cuda.is_available():
model.cuda()
state = state.cuda()
replay_memory = deque(maxlen=opt.replay_memory_size)
epoch = 0
while epoch < opt.num_epochs:
next_steps = env.get_next_states()
# Exploration or exploitation
#############
# epsilon = A threshhold that decides how likely it is that a random action is performed. also often calles "eps_threshold"
# Insert a function that creates a decaying value per epoch, between 1 and 0. amount of epochs is defined as input, and default in parser(top of this file)
# The best results will probably come from a function that decays in the first x epochs and is = "some final low value, e.g. 0.001" for the last y epochs
#############
epsilon = #.....
#############
# u = a random number between 0 and 1 that randomly decides whether a random action is performed or an action chosen by the model
#############
u = #.....
random_action = u <= epsilon
next_actions, next_states = zip(*next_steps.items())
next_states = torch.stack(next_states)
# if cuda is available, move network to GPU
if torch.cuda.is_available():
next_states = next_states.cuda()
model.#...... evaluate model, built in function
with torch.no_grad():
predictions = model(next_states)[:, 0]
model.train()
if random_action:
index = randint(0, len(next_steps) - 1)
else:
index = torch.argmax(predictions).item()
next_state = next_states[index, :]
action = next_actions[index]
#############
# reward = metric of how well the model has performed
# done = whether or not the model is finished with the current game
# a function that outputs both of these state variables can be found in src/tetris.py
# remeber to pass in the second argument as well, otherwise all training will be done with visualization which is cool to watch, but slow
#############
reward, done = env.#.....
if torch.cuda.is_available():
next_state = next_state.cuda()
replay_memory.append([state, reward, next_state, done])
if done:
final_score = env.score
final_tetrominoes = env.tetrominoes
final_cleared_lines = env.cleared_lines
state = env.reset()
if torch.cuda.is_available():
state = state.cuda()
else:
state = next_state
continue
if len(replay_memory) < opt.replay_memory_size / 10:
continue
#..... Increment epoch counter
batch = sample(replay_memory, min(len(replay_memory), opt.batch_size))
state_batch, reward_batch, next_state_batch, done_batch = zip(*batch)
state_batch = torch.stack(tuple(state for state in state_batch))
reward_batch = torch.from_numpy(np.array(reward_batch, dtype=np.float32)[:, None])
next_state_batch = torch.stack(tuple(state for state in next_state_batch))
# if cuda is available, move network to GPU
if torch.cuda.is_available():
state_batch = state_batch.cuda()
reward_batch = reward_batch.cuda()
next_state_batch = next_state_batch.cuda()
q_values = model(state_batch)
model.#...... evaluate model, built in function
with torch.no_grad():
next_prediction_batch = model(next_state_batch)
model.train()
y_batch = torch.cat(
tuple(reward if done else reward + opt.gamma * prediction for reward, done, prediction in
zip(reward_batch, done_batch, next_prediction_batch)))[:, None]
#############
# Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses
#
# In order to ensure that behaviour learned from one epoch is not used in multiple epochs to tune a network(accumulating gradients),
# we need to set all gradients to zero, this is a built in function
#############
optimizer.#.....
#############
# A criterion is a loss function that compares two inputs as tensors(pytorch datatype)
#
# Pass in two tensors into our criterion(loss function, set to MSELoss())
#############
loss = criterion()#(tensorX, tensorY)
#############
# The criterion outputs a loss object that stores the loss value and enables us to tune our neural network by backpropagation
#
# The loss function has access to a built in function that does this
#############
loss.#.....
#############
# Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses
#
# The optimizer should her e perform a parameter update based on the current gradient, and does this by a built in function
#############
optimizer.#.....
print("Epoch: {}/{}, Action: {}, Score: {}, Tetrominoes {}, Cleared lines: {}".format(
epoch,
opt.num_epochs,
action,
final_score,
final_tetrominoes,
final_cleared_lines))
writer.add_scalar('Train/Score', final_score, epoch - 1)
writer.add_scalar('Train/Tetrominoes', final_tetrominoes, epoch - 1)
writer.add_scalar('Train/Cleared lines', final_cleared_lines, epoch - 1)
if epoch > 0 and epoch % opt.save_interval == 0:
torch.save(model, "{}/tetris_{}".format(opt.saved_path, epoch))
#############
# PyTorch offers a way of saving either full models or a model state's weights as a file
#
# Search on pytorch.org for this save function
#############
torch.#.....
def train(opt):
if torch.cuda.is_available():
torch.cuda.manual_seed(123)
else:
torch.manual_seed(123)
if os.path.isdir(opt.log_path):
shutil.rmtree(opt.log_path)
os.makedirs(opt.log_path)
writer = SummaryWriter(opt.log_path)
env = Tetris(width=opt.width, height=opt.height, block_size=opt.block_size)
model = DeepQNetwork()
model_target = DeepQNetwork()
optimizer = torch.optim.Adam(model.parameters(), lr=opt.lr)
criterion = nn.MSELoss()
state = env.reset()
if torch.cuda.is_available():
model.cuda()
model_target.cuda()
state = state.cuda()
if opt.PER:
replay_memory = Memory(capacity=opt.replay_memory_size)
else:
replay_memory = deque(maxlen=opt.replay_memory_size)
epoch = 0
warmup_epoch = 0
while epoch < opt.num_epochs:
next_steps = env.get_next_states()
# Exploration or exploitation
epsilon = opt.final_epsilon + (
max(opt.num_decay_epochs - epoch, 0) *
(opt.initial_epsilon - opt.final_epsilon) / opt.num_decay_epochs)
u = random()
random_action = u <= epsilon
next_actions, next_states = zip(*next_steps.items())
next_states = torch.stack(next_states)
if torch.cuda.is_available():
next_states = next_states.cuda()
model.eval()
with torch.no_grad():
predictions = model(next_states)[:, 0]
model.train()
if random_action:
index = randint(0, len(next_steps) - 1)
else:
index = torch.argmax(predictions).item()
next_state = next_states[index, :]
action = next_actions[index]
reward, done = env.step(action, render=True)
if torch.cuda.is_available():
next_state = next_state.cuda()
if opt.PER:
experience = state, action, reward, next_state, done
replay_memory.store(experience)
else:
replay_memory.append([state, reward, next_state, done])
if done:
final_score = env.score
final_tetrominoes = env.tetrominoes
final_cleared_lines = env.cleared_lines
state = env.reset()
if torch.cuda.is_available():
state = state.cuda()
else:
state = next_state
continue
warmup_epoch += 1
if warmup_epoch < opt.learning_starts:
continue
epoch += 1
if opt.PER:
tree_idx, batch = replay_memory.sample(opt.batch_size)
else:
batch = sample(replay_memory,
min(len(replay_memory), opt.batch_size))
state_batch, _, reward_batch, next_state_batch, done_batch = zip(
*batch)
state_batch = torch.stack(tuple(state for state in state_batch))
reward_batch = torch.from_numpy(
np.array(reward_batch, dtype=np.float32)[:, None])
next_state_batch = torch.stack(
tuple(state for state in next_state_batch))
if torch.cuda.is_available():
state_batch = state_batch.cuda()
reward_batch = reward_batch.cuda()
next_state_batch = next_state_batch.cuda()
q_values = model(state_batch)
model_target.eval()
with torch.no_grad():
next_prediction_batch = model_target(next_state_batch)
model_target.train()
y_batch = torch.cat(
tuple(reward if done else reward + opt.gamma * prediction
for reward, done, prediction in zip(
reward_batch, done_batch, next_prediction_batch)))[:,
None]
optimizer.zero_grad()
loss = criterion(q_values, y_batch)
loss.backward()
optimizer.step()
model.eval()
model_target.eval()
if opt.PER:
with torch.no_grad():
if torch.cuda.is_available():
replay_memory.batch_update(
tree_idx,
np.abs(q_values.detach().cpu().numpy() -
y_batch.cpu().numpy()))
else:
replay_memory.batch_update(
tree_idx,
np.abs(q_values.detach().numpy() - y_batch.numpy()))
# Update target model <- model
if epoch % opt.target_update_freq == 0:
with torch.no_grad():
model_target.load_state_dict(model.state_dict())
model_target.train()
model.eval()
print(
"Epoch: {}/{}, Action: {}, Score: {}, Tetrominoes {}, Cleared lines: {}"
.format(epoch, opt.num_epochs, action, final_score,
final_tetrominoes, final_cleared_lines))
writer.add_scalar('Train/Score', final_score, epoch - 1)
writer.add_scalar('Train/Tetrominoes', final_tetrominoes, epoch - 1)
writer.add_scalar('Train/Cleared lines', final_cleared_lines,
epoch - 1)
if (epoch > 0
and epoch % opt.save_interval) == 0 or final_score >= 10000.0:
torch.save(model, "{}/tetris_{}".format(opt.saved_path, epoch))
torch.save(model, "{}/tetris".format(opt.saved_path))
def train(opt):
# Set random seed
if torch.cuda.is_available():
torch.cuda.manual_seed(opt.random_seed)
else:
torch.manual_seed(opt.random_seed)
# Instantiate the model
if opt.conv_dim is not None and \
opt.conv_kernel_sizes is not None and \
opt.conv_strides is not None and \
opt.fc_dim is not None:
model = DeepQNetwork(opt.image_size,
opt.image_size,
conv_dim=opt.conv_dim,
conv_kernel_sizes=opt.conv_kernel_sizes,
conv_strides=opt.conv_strides,
fc_dim=opt.fc_dim)
else:
model = DeepQNetwork(opt.image_size, opt.image_size)
if opt.log_comet_ml:
# Create a Comet.ml experiment
experiment = Experiment(api_key=opt.comet_ml_api_key,
project_name=opt.comet_ml_project_name,
workspace=opt.comet_ml_workspace)
experiment.log_other("iters_to_save", opt.iters_to_save)
experiment.log_other("completed", False)
experiment.log_other("random_seed", opt.random_seed)
# Report hyperparameters to Comet.ml
hyper_params = {
"image_size": opt.image_size,
"batch_size": opt.batch_size,
"optimizer": opt.optimizer,
"learning_rate": opt.lr,
"gamma": opt.gamma,
"initial_epsilon": opt.initial_epsilon,
"final_epsilon": opt.final_epsilon,
"num_iters": opt.num_iters,
"replay_memory_size": opt.replay_memory_size,
"random_seed": opt.random_seed,
"conv_dim": opt.conv_dim,
"conv_kernel_sizes": opt.conv_kernel_sizes,
"conv_strides": opt.conv_strides,
"fc_dim": opt.fc_dim
}
experiment.log_parameters(hyper_params)
optimizer = torch.optim.Adam(model.parameters(),
lr=1e-6) # Optimization algorithm
criterion = nn.MSELoss() # Loss function
game_state = FlappyBird() # Instantiate the Flappy Compass game
image, reward, terminal = game_state.next_frame(
0
) # Get the next image, along with its reward and an indication if it's a terminal state
# Image preprocessing step (scaling, color removal and convertion to a PyTorch tensor)
image = pre_processing(
image[:game_state.screen_width, :int(game_state.base_y)],
opt.image_size, opt.image_size)
image = torch.from_numpy(image)
# Move the model and the current image data to the GPU, if available
if torch.cuda.is_available():
model.cuda()
image = image.cuda()
# Prepare the state variable, which will host the last 4 frames
state = torch.cat(tuple(image for _ in range(4)))[None, :, :, :]
# Initialize the replay memory, which saves sets of consecutive game states, the reward and terminal state indicator
# so that the model can learn from them (essentially constitutes the training data, which grows with every new iteration)
replay_memory = []
iter = 0 # Iteration counter
# Main training loop performing the number of iterations specified by num_iters
while iter < opt.num_iters:
prediction = model(state)[0] # Get a prediction from the current state
epsilon = opt.final_epsilon + (
(opt.num_iters - iter) *
(opt.initial_epsilon - opt.final_epsilon) / opt.num_iters
) # Set the decay of the probability of random actions
u = random()
random_action = u <= epsilon
if random_action:
print("Perform a random action")
action = randint(0, 1)
else:
# Use the model's prediction to decide the next action
action = torch.argmax(prediction).item()
# Get a new frame and process it
next_image, reward, terminal = game_state.next_frame(action)
next_image = pre_processing(
next_image[:game_state.screen_width, :int(game_state.base_y)],
opt.image_size, opt.image_size)
next_image = torch.from_numpy(next_image)
# Move the next image data to the GPU, if available
if torch.cuda.is_available():
next_image = next_image.cuda()
next_state = torch.cat(
(state[0, 1:, :, :], next_image)
)[None, :, :, :] # Prepare the next state variable, which will host the last 4 frames
replay_memory.append(
[state, action, reward, next_state, terminal]
) # Save the current state, action, next state and terminal state indicator in the replay memory
if len(replay_memory) > opt.replay_memory_size:
del replay_memory[
0] # Delete the oldest reolay from memory if full capacity has been reached
batch = sample(replay_memory, min(len(replay_memory), opt.batch_size)
) # Retrieve past play sequences from the replay memory
state_batch, action_batch, reward_batch, next_state_batch, terminal_batch = zip(
*batch)
state_batch = torch.cat(tuple(
state for state in state_batch)) # States of the current batch
action_batch = torch.from_numpy(
np.array([[1, 0] if action == 0 else [0, 1]
for action in action_batch],
dtype=np.float32)) # Actions taken in the current batch
reward_batch = torch.from_numpy(
np.array(reward_batch,
dtype=np.float32)[:,
None]) # Rewards in the current batch
next_state_batch = torch.cat(tuple(
state
for state in next_state_batch)) # Next states of the current batch
# Move batch data to the GPU, if available
if torch.cuda.is_available():
state_batch = state_batch.cuda()
action_batch = action_batch.cuda()
reward_batch = reward_batch.cuda()
next_state_batch = next_state_batch.cuda()
current_prediction_batch = model(
state_batch
) # Predictions of the model for the replays of the current batch
next_prediction_batch = model(
next_state_batch
) # Next predictions of the model for the replays of the current batch
# Set ground truth for the rewards for the current batch, considering whether the state is terminal or not
y_batch = torch.cat(
tuple(reward if terminal else reward +
opt.gamma * torch.max(prediction)
for reward, terminal, prediction in zip(
reward_batch, terminal_batch, next_prediction_batch)))
q_value = torch.sum(
current_prediction_batch * action_batch, dim=1
) # Predicted Q values (i.e. estimated return for each action)
optimizer.zero_grad(
) # Reset the gradients to zero before a new optimization step
loss = criterion(q_value, y_batch) # Calculate the loss
loss.backward() # Backpropagation
optimizer.step() # Weights optimization step
state = next_state # Move to the next frame
iter += 1
print(
"Iteration: {}/{}, Action: {}, Loss: {}, Epsilon {}, Reward: {}, Q-value: {}"
.format(iter + 1, opt.num_iters, action, loss, epsilon, reward,
torch.max(prediction)))
if opt.log_comet_ml:
# Log metrics to Comet.ml
experiment.log_metric("train_loss", loss, step=iter)
experiment.log_metric("train_epsilon", epsilon, step=iter)
experiment.log_metric("train_reward", reward, step=iter)
experiment.log_metric("train_Q_value",
torch.max(prediction),
step=iter)
if (iter + 1) % opt.iters_to_save == 0:
# Get the current day and time to attach to the saved model's name
current_datetime = datetime.now().strftime('%d_%m_%Y_%H_%M')
# Set saved model name
model_filename = f'{opt.saved_path}/flappy_compass_{current_datetime}_{iter+1}.pth'
# Save model every iters_to_save iterations
torch.save(model, model_filename)
if opt.log_comet_ml and opt.comet_ml_save_model:
# Upload model to Comet.ml
experiment.log_asset(file_path=model_filename, overwrite=True)
# Get the current day and time to attach to the saved model's name
current_datetime = datetime.now().strftime('%d_%m_%Y_%H_%M')
# Set saved model name
model_filename = f'{opt.saved_path}/flappy_compass_{current_datetime}_{iter+1}.pth'
# Save the model after reaching the final iteration
torch.save(model, model_filename)
if opt.log_comet_ml:
# Only report that the experiment completed successfully if it finished the training without errors
experiment.log_other("completed", True)
if opt.comet_ml_save_model:
# Upload model to Comet.ml
experiment.log_asset(file_path=model_filename, overwrite=True)
def train(opt):
if torch.cuda.is_available():
torch.cuda.manual_seed(123)
else:
torch.manual_seed(123)
model = DeepQNetwork()
if os.path.isdir(opt.log_path):
shutil.rmtree(opt.log_path)
os.makedirs(opt.log_path)
writer = SummaryWriter(opt.log_path)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-6)
criterion = nn.MSELoss()
game_state = NS_SHAFT()
image, reward, terminal = game_state.next_frame(0)
imgplot = plt.imshow(image)
image = pre_processing(image[:game_state.screen_width, :int(400)],
opt.image_size, opt.image_size)
image = torch.from_numpy(image)
if torch.cuda.is_available():
model.cuda()
image = image.cuda()
state = torch.cat(tuple(image for _ in range(4)))[None, :, :, :]
replay_memory = []
iter = 0
while iter < opt.num_iters:
#plt.plot([1,2,3,4])
prediction = model(state)
# Exploration or exploitation
epsilon = opt.final_epsilon + (
(opt.num_iters - iter) *
(opt.initial_epsilon - opt.final_epsilon) / opt.num_iters)
u = random()
random_action = u <= epsilon
if random_action:
print("Perform a random action")
action = randint(-1, 1) * 4
else:
#print('prediction',prediction)
action = (prediction.data.max(1)[1].item() - 1) * 4
#print('a',action)
next_image, reward, terminal = game_state.next_frame(action)
next_image = pre_processing(
next_image[:game_state.screen_width, :int(400)], opt.image_size,
opt.image_size)
next_image = torch.from_numpy(next_image)
if torch.cuda.is_available():
next_image = next_image.cuda()
next_state = torch.cat((state[0, 1:, :, :], next_image))[None, :, :, :]
replay_memory.append([state, action, reward, next_state, terminal])
if len(replay_memory) > opt.replay_memory_size:
del replay_memory[0]
batch = sample(replay_memory, min(len(replay_memory), opt.batch_size))
state_batch, action_batch, reward_batch, next_state_batch, terminal_batch = zip(
*batch)
state_batch = torch.cat(tuple(state for state in state_batch))
action_batch_tmp = []
for action in action_batch:
if action == -4:
action_batch_tmp.append([1, 0, 0])
elif action == 0:
action_batch_tmp.append([0, 1, 0])
else:
action_batch_tmp.append([0, 0, 1])
action_batch = torch.from_numpy(
np.array(action_batch_tmp, dtype=np.float32))
reward_batch = torch.from_numpy(
np.array(reward_batch, dtype=np.float32)[:, None])
next_state_batch = torch.cat(tuple(state
for state in next_state_batch))
if torch.cuda.is_available():
state_batch = state_batch.cuda()
action_batch = action_batch.cuda()
reward_batch = reward_batch.cuda()
next_state_batch = next_state_batch.cuda()
current_prediction_batch = model(state_batch)
next_prediction_batch = model(next_state_batch)
y_batch = torch.cat(
tuple(reward if terminal else reward +
opt.gamma * torch.max(prediction)
for reward, terminal, prediction in zip(
reward_batch, terminal_batch, next_prediction_batch)))
q_value = torch.sum(current_prediction_batch * action_batch, dim=1)
optimizer.zero_grad()
# y_batch = y_batch.detach()
loss = criterion(q_value, y_batch)
loss.backward()
optimizer.step()
state = next_state
iter += 1
print(
"Iteration: {}/{}, Action: {}, Loss: {}, Epsilon {}, Reward: {}, Q-value: {}"
.format(iter + 1, opt.num_iters, action, loss, epsilon, reward,
torch.max(prediction)))
writer.add_scalar('Train/Loss', loss, iter)
writer.add_scalar('Train/Epsilon', epsilon, iter)
writer.add_scalar('Train/Reward', reward, iter)
writer.add_scalar('Train/Q-value', torch.max(prediction), iter)
if iter + 1 == opt.num_iters:
torch.save(model, "{}/ns_shaft_{}".format(opt.saved_path,
iter + 1))
torch.save(model, "{}/ns_shaft".format(opt.saved_path))
def training(arguments):
if torch.cuda.is_available():
torch.cuda.manual_seed(123)
else:
torch.manual_seed(123)
model = DeepQNetwork()
if os.path.isdir(arguments.log_path):
shutil.rmtree(arguments.log_path)
os.makedirs(arguments.log_path)
writer = SummaryWriter(arguments.log_path)
optimiser = torch.optim.Adam(model.parameters(), lr=1e-6)
criterion = nn.MSELoss()
gameState = Flappyplayer()
image, reward, terminal = gameState.next_frame(0)
image = pre_processing(image[:gameState.SCREENW, :int(gameState.base_y)], arguments.image_size,
arguments.image_size)
image = torch.from_numpy(image)
if torch.cuda.is_available():
model.cuda()
image = image.cuda()
state = torch.cat(tuple(image for _ in range(4)))[None, :, :, :]
replay_mem = []
iter = 0
while iter < arguments.iters:
prediction = model(state)[0]
# Exploration or exploitation
epsilon = arguments.final_epsilon + (
(arguments.iters - iter) * (arguments.initial_epsilon - arguments.final_epsilon) / arguments.iters)
u = random()
random_action = u <= epsilon
if random_action:
print("Perform a random action")
action = 1
print(action)
else:
action = 0
nextImage, reward, terminal = gameState.next_frame(action)
nextImage = pre_processing(nextImage[:gameState.SCREENW, :int(gameState.base_y)], arguments.image_size,
arguments.image_size)
nextImage = torch.from_numpy(nextImage)
if torch.cuda.is_available():
nextImage = nextImage.cuda()
nextState = torch.cat((state[0, 1:, :, :], nextImage))[None, :, :, :]
replay_mem.append([state, action, reward, nextState, terminal])
if len(replay_mem) > arguments.replay_mem:
del replay_mem[0]
batch = sample(replay_mem, min(len(replay_mem), arguments.batch_size))
stateBatch, actionBatch, rewardBatch, nextStateBatch, terminalBatch = zip(*batch)
stateBatch = torch.cat(tuple(state for state in stateBatch))
actionBatch = torch.from_numpy(np.array([[1,0] if action == 0 else [0,1] for action in actionBatch], dtype=np.float32))
rewardBatch = torch.from_numpy(np.array(rewardBatch, dtype=np.float32)[:, None])
nextStateBatch = torch.cat(tuple(state for state in nextStateBatch))
if torch.cuda.is_available():
stateBatch = stateBatch.cuda()
actionBatch = actionBatch.cuda()
rewardBatch = rewardBatch.cuda()
nextStateBatch = nextStateBatch.cuda()
currentPredBatch = model(stateBatch)
nextPredBatch = model(nextStateBatch)
yBatch = torch.cat(tuple(reward if terminal else reward + arguments.gamma * torch.max(prediction) for reward, terminal, prediction in zip(rewardBatch, terminalBatch, nextPredBatch)))
qValue = torch.sum(currentPredBatch*actionBatch, dim=1)
optimiser.zero_grad()
loss = criterion(qValue, yBatch)
loss.backward()
optimiser.step()
state = nextState
iter +=1
print("Iteration: {}/{}, Action: {}, Loss: {}, Epsilon: {}, Reward: {}, Q-Value: {}".format(iter+1,arguments.iters, action, loss, epsilon, reward, torch.max(prediction)))
writer.add_scalar('Train/Loss', loss, iter)
writer.add_scalar('Train/Epsilon', epsilon, iter)
writer.add_scalar('Train/Reward', reward, iter)
writer.add_scalar('Train/Q-Value', torch.max(prediction), iter)
if (iter+1) % 1000000 == 0:
torch.save(model, "{}/flappy_bird_{}".format(arguments.saved_path, iter+1))
torch.save(model, "{}/flappy_bird".format(arguments.saved_path))