def play_game(options):
"""Play flappy bird with pretrained dqn model
weight -- model file name containing weight of dqn
best -- if the model is best or not
"""
model = QNetwork()
if options.ckpt_path is None:
print ('you should give weight file name.')
return
print ('load previous model weight: {}'.format(options.ckpt_path))
episode, epsilon = load_checkpoint(options.ckpt_path, model)
if options.cuda:
model = model.cuda()
algorithm = DQN(model, optim, epsilon, options)
algorithm.set_eval()
bird_game = game.GameState()
bird_game.FPS = 480
action = [1, 0]
o, r, terminal = bird_game.frame_step(action)
o = preprocess(o)
rpm = ReplayMemory(1, options)
rpm.append(o, action, r, terminal)
start = time.time()
fc = 0
score = 0
while True:
prev_o, a, r, o, terminal = rpm.sample(1)
# q = algorithm(o).cpu().detach().numpy()[0]
score = max(score, bird_game.score)
action = algorithm.get_optim_action(o)
o, r, terminal = bird_game.frame_step(action)
o = preprocess(o)
# img = Image.fromarray((o*255).astype(np.uint8)).convert(mode='L')
# img.save(f'{fc}-{r}-{q.argmax()}.png')
# fc += 1
if terminal or score > options.max_score*2:
break
rpm.append(o, action, r, terminal)
ela = time.time() - start
print(f'Final Score {score}, FPS{bird_game.FPS}, {ela//60}m{ela%60}s')
# if __name__ == "__main__":
# main()
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, seed):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
if torch.cuda.is_available():
self.qnetwork_local = self.qnetwork_local.cuda()
self.qnetwork_target = self.qnetwork_target.cuda()
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0.):
"""Returns actions for given state as per current policy.
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.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local(state)
self.qnetwork_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):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# Get max predicted Q values (for next states) from target model
Q_targets_next = self.qnetwork_target(next_states).detach().max(
1)[0].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.qnetwork_local(states).gather(1, actions)
# Compute loss
loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied 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)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self,
state_size,
action_size,
seed,
fc1_units=64,
fc2_units=64,
fc3_units=None,
double_q=False):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.double_q = double_q
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size, seed,
fc1_units, fc2_units,
fc3_units).to(device)
self.qnetwork_target = QNetwork(state_size, action_size, seed,
fc1_units, fc2_units,
fc3_units).to(device)
if torch.cuda.is_available():
self.qnetwork_local.cuda()
self.qnetwork_target.cuda()
else:
self.qnetwork_local.cpu()
self.qnetwork_target.cpu()
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),
lr=LR,
weight_decay=WD)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def get_action(self, state, eps=0.):
state = torch.from_numpy(state).float().unsqueeze(0).to(device)
with torch.no_grad():
output = self.qnetwork_local.forward(state)
action_values = self.qnetwork_local.forward(state)
if random.random() <= eps:
return np.random.choice(np.arange(self.action_size))
else:
return output.argmax().item()
def learn(self, experiences, gamma):
states, actions, rewards, next_states, dones = experiences
# double q learning
argmax_a = self.qnetwork_local.forward(next_states).detach().argmax(
dim=1).unsqueeze(dim=1)
a_val = self.qnetwork_target.forward(next_states).detach()
Q_targets_next = a_val.gather(1, argmax_a)
Q_targets = rewards + GAMMA * Q_targets_next
Q_expected = self.qnetwork_local.forward(states).gather(1, actions)
loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ------------------- update target network ------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model (PyTorch model): weights will be copied from
target_model (PyTorch model): weights will be copied 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 train_dqn(options):
max_episode = options.max_episode
flappyBird = game.GameState()
print(f'FPS {flappyBird.FPS}')
rpm = ReplayMemory(options.rpm_size, options) # DQN的经验回放池
model = QNetwork()
if options.resume and options.ckpt_path is not None:
print ('load previous model weight: {}'.format(options.ckpt_path))
episode, epsilon = load_checkpoint(options.ckpt_path, model)
else:
epsilon = options.init_e
episode = 0
if options.cuda:
model = model.cuda()
optimizer = optim.Adam(model.parameters(), lr=options.lr)
algorithm = DQN(model, optimizer, epsilon, options)
# 先往经验池里存一些数据，避免最开始训练的时候样本丰富度不够
while len(rpm) < options.rpm_size/4:
run_episode(algorithm, flappyBird, rpm, options)
print(f'observation done {len(rpm)}')
# 开始训练
logname = time.strftime('%Y-%m-%d %M-%I-%S' , time.localtime())
logger = get_logger(f'log/{logname}.log')
best_reward = 0
max_score = 0
begin = time.time()
while episode < max_episode: # 训练max_episode个回合，test部分不计算入episode数量
# train part
reward, loss, score = run_episode(algorithm, flappyBird, rpm, options)
algorithm.epsilon = max(algorithm.final_e, algorithm.epsilon - algorithm.e_decrement)
episode += 1
max_score = max(max_score, score)
if (episode)%10 == 0:
logger.info('episode:[{}/{}]\tscore:{:.3f}\ttrain_reward:{:.5f}\tloss:{:.5f}'.format(
episode, max_episode, score, reward, loss))
# test part
if (episode)%options.evaluate_freq == 0:
eval_reward, score = evaluate(flappyBird, algorithm, options)
mid = time.time()
elapsed = round(mid-begin)
logger.info('episode:[{}/{}]\tscore:{:.3f}\tepsilon:{:.5f}\ttest_reward:{:.5f}\t{}:{}'.format(
episode, max_episode, score, algorithm.epsilon, eval_reward, elapsed//60, elapsed%60))
if eval_reward > best_reward:
save_path = f'ckpt/best_{score}.ckpt'
save_checkpoint({
'episode': episode,
'epsilon': algorithm.epsilon,
'state_dict': model.state_dict(),
}, False, save_path
)
if (episode)%1000 == 0:
save_path = f'ckpt/episode_{episode}.ckpt'
save_checkpoint({
'episode': episode,
'epsilon': algorithm.epsilon,
'state_dict': model.state_dict(),
}, False, save_path
)
# 训练结束，保存模型
save_path = f'ckpt/final_{episode}_{score}.ckpt'
save_checkpoint({
'episode': episode,
'epsilon': algorithm.epsilon,
'state_dict': model.state_dict(),
}, False, save_path)
mid = time.time()
elapsed = round(mid-begin)
logger.info('training completed, {} episiode, {}m {}s'.format(max_episode, elapsed//60, elapsed%60))
print(f'max_score {max_score}')
class Agent():
def __init__(self, state_size, action_size, random_seed):
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
# Q-Network
self.qnetwork_local = QNetwork(state_size, action_size, random_seed)
self.qnetwork_target = QNetwork(state_size, action_size, random_seed)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(), lr=LR)
# Move to GPU if CUDA is available
if train_on_gpu:
self.qnetwork_local.cuda()
self.qnetwork_target.cuda()
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
random_seed)
self.t_step = 0
def step(self, state, action, reward, next_state, done):
# Save experience / reward
self.memory.add(state, action, reward, next_state, done)
self.t_step = (self.t_step + 1) % UPDATE_EVERY
if self.t_step == 0:
# Learn, if enough samples are available in memory
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, eps=0):
"""Returns actions for given state as per current policy."""
state = torch.from_numpy(state).float().unsqueeze(0)
if train_on_gpu:
state = state.cuda()
self.qnetwork_local.eval()
action_values = self.qnetwork_local(Variable(state, volatile=True))
self.qnetwork_local.train()
max_action = np.argmax(action_values.cpu().data.numpy())
policy_s = np.ones(self.action_size) * eps / self.action_size
policy_s[max_action] = 1 - eps + (eps / self.action_size)
action = np.random.choice(np.arange(self.action_size), p=policy_s)
return action
def learn(self, experiences, gamma):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# Get max predicted Q values from target model
Q_targets_next = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
# Compute Q targets for current states
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Compute loss
Q_expected = self.qnetwork_local(states).gather(1, actions)
loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
# ----------------------- update target network ----------------------- #
self.soft_update(self.qnetwork_local, self.qnetwork_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model: PyTorch model (weights will be copied from)
target_model: PyTorch model (weights will be copied 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)
