class DQN(object):
def __init__(self,state_space,action_space,seed,update_every,batch_size,buffer_size,learning_rate):
self.action_space = action_space
self.state_space = state_space
self.seed = random.seed(seed)
self.batch_size = batch_size
self.buffer_size = buffer_size
self.learning_rate = learning_rate
self.update_every = update_every
self.qnetwork_local = QNetwork(state_space,action_space)
self.qnetwork_target = QNetwork(state_space,action_space)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),lr=learning_rate)
# Initialize replaybuffer
self.memory = ReplayBuffer(action_space,buffer_size,buffer_size,seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self,state,action,reward,next_state,done,GAMMA):
# Save the experience
self.memory.add_experience(state,action,reward,next_state,done)
# learn from the experience
self.t_step = (self.t_step + 1) % self.update_every
if self.t_step == 0:
if len(self.memory) > self.buffer_size:
experiences = self.memory.sample()
self.learn(experiences,GAMMA)
def act(self,state,eps=0.):
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.sample(np.arange(self.action_space))
def learn(self,experiences,GAMMA):
states,actions,rewards,next_states,dones = experiences
target_values = self.qnetwork_target(next_states).detach().max(1)[0].unsqueeze(1)
targets = reward + (GAMMA * target_values * (1-done))
action_values = self.qnetwork_local(states).gather(1,actions)
loss = F.mse_loss(action_values,targets)
loss.backward()
self.optimizer.step()
soft_update(TAU)
def soft_update(self,tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
"""
for local_param,target_param in zip(self.qnetwork_local.parameters(),self.qnetwork_target.parameters()):
local_param.data.copy_(tau*local_param.data + (1-tau)*target_param.data)
# self.qnetwork_local.parameters() = TAU*self.qnetwork_local.parameters() + (1-TAU)*self.qnetwork_target.parameters()
class Agent:
def __init__(
self,
env: 'Environment',
input_frame: ('int: the number of channels of input image'),
input_dim: (
'int: the width and height of pre-processed input image'),
num_frames: ('int: Total number of frames'),
eps_decay: ('float: Epsilon Decay_rate'),
gamma: ('float: Discount Factor'),
target_update_freq: ('int: Target Update Frequency (by frames)'),
update_type: (
'str: Update type for target network. Hard or Soft') = 'hard',
soft_update_tau: ('float: Soft update ratio') = None,
batch_size: ('int: Update batch size') = 32,
buffer_size: ('int: Replay buffer size') = 1000000,
update_start_buffer_size: (
'int: Update starting buffer size') = 50000,
learning_rate: ('float: Learning rate') = 0.0004,
eps_min: ('float: Epsilon Min') = 0.1,
eps_max: ('float: Epsilon Max') = 1.0,
device_num: ('int: GPU device number') = 0,
rand_seed: ('int: Random seed') = None,
plot_option: ('str: Plotting option') = False,
model_path: ('str: Model saving path') = './'):
self.action_dim = env.action_space.n
self.device = torch.device(
f'cuda:{device_num}' if torch.cuda.is_available() else 'cpu')
self.model_path = model_path
self.env = env
self.input_frames = input_frame
self.input_dim = input_dim
self.num_frames = num_frames
self.epsilon = eps_max
self.eps_decay = eps_decay
self.eps_min = eps_min
self.gamma = gamma
self.target_update_freq = target_update_freq
self.update_cnt = 0
self.update_type = update_type
self.tau = soft_update_tau
self.batch_size = batch_size
self.buffer_size = buffer_size
self.update_start = update_start_buffer_size
self.seed = rand_seed
self.plot_option = plot_option
self.q_current = QNetwork(
(self.input_frames, self.input_dim, self.input_dim),
self.action_dim).to(self.device)
self.q_target = QNetwork(
(self.input_frames, self.input_dim, self.input_dim),
self.action_dim).to(self.device)
self.q_target.load_state_dict(self.q_current.state_dict())
self.q_target.eval()
self.optimizer = optim.Adam(self.q_current.parameters(),
lr=learning_rate)
self.memory = ReplayBuffer(
self.buffer_size,
(self.input_frames, self.input_dim, self.input_dim),
self.batch_size)
def select_action(
self, state:
'Must be pre-processed in the same way while updating current Q network. See def _compute_loss'
):
if np.random.random() < self.epsilon:
return np.zeros(self.action_dim), self.env.action_space.sample()
else:
# if normalization is applied to the image such as devision by 255, MUST be expressed 'state/255' below.
state = torch.FloatTensor(state).to(self.device).unsqueeze(0) / 255
Qs = self.q_current(state)
action = Qs.argmax()
return Qs.detach().cpu().numpy(), action.detach().item()
def processing_resize_and_gray(self, frame):
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY) # Pure
# frame = cv2.cvtColor(frame[:177, 32:128, :], cv2.COLOR_RGB2GRAY) # Boxing
# frame = cv2.cvtColor(frame[2:198, 7:-7, :], cv2.COLOR_RGB2GRAY) # Breakout
frame = cv2.resize(frame,
dsize=(self.input_dim, self.input_dim)).reshape(
self.input_dim, self.input_dim).astype(np.uint8)
return frame
def get_state(self, action, skipped_frame=0):
'''
num_frames: how many frames to be merged
input_size: hight and width of input resized image
skipped_frame: how many frames to be skipped
'''
next_state = np.zeros(
(self.input_frames, self.input_dim, self.input_dim))
rewards = 0
dones = 0
for i in range(self.input_frames):
for j in range(skipped_frame):
state, reward, done, _ = self.env.step(action)
rewards += reward
dones += int(done)
state, reward, done, _ = self.env.step(action)
next_state[i] = self.processing_resize_and_gray(state)
rewards += reward
dones += int(done)
return rewards, next_state, dones
def get_init_state(self):
state = self.env.reset()
action = self.env.action_space.sample()
_, state, _ = self.get_state(action, skipped_frame=0)
return state
def store(self, state, action, reward, next_state, done):
self.memory.store(state, action, reward, next_state, done)
def update_current_q_net(self):
batch = self.memory.batch_load()
loss = self._compute_loss(batch)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
return loss.item()
def target_soft_update(self):
for target_param, current_param in zip(self.q_target.parameters(),
self.q_current.parameters()):
target_param.data.copy_(self.tau * current_param.data +
(1.0 - self.tau) * target_param.data)
def target_hard_update(self):
self.update_cnt = (self.update_cnt + 1) % self.target_update_freq
if self.update_cnt == 0:
self.q_target.load_state_dict(self.q_current.state_dict())
def train(self):
tic = time.time()
losses = []
scores = []
epsilons = []
avg_scores = [[-1000]]
score = 0
print("Storing initial buffer..")
state = self.get_init_state()
for frame_idx in range(1, self.update_start + 1):
_, action = self.select_action(state)
reward, next_state, done = self.get_state(action, skipped_frame=0)
self.store(state, action, reward, next_state, done)
state = next_state
if done: state = self.get_init_state()
print("Done. Start learning..")
history_store = []
for frame_idx in range(1, self.num_frames + 1):
Qs, action = self.select_action(state)
reward, next_state, done = self.get_state(action, skipped_frame=0)
self.store(state, action, reward, next_state, done)
history_store.append([state, Qs, action, reward, next_state, done])
loss = self.update_current_q_net()
if self.update_type == 'hard': self.target_hard_update()
elif self.update_type == 'soft': self.target_soft_update()
score += reward
losses.append(loss)
if done:
scores.append(score)
if np.mean(scores[-10:]) > max(avg_scores):
torch.save(
self.q_current.state_dict(),
self.model_path + '{}_Score:{}.pt'.format(
frame_idx, np.mean(scores[-10:])))
training_time = round((time.time() - tic) / 3600, 1)
np.save(
self.model_path +
'{}_history_Score_{}_{}hrs.npy'.format(
frame_idx, score, training_time),
np.array(history_store))
print(
" | Model saved. Recent scores: {}, Training time: {}hrs"
.format(scores[-10:], training_time),
' /'.join(os.getcwd().split('/')[-3:]))
avg_scores.append(np.mean(scores[-10:]))
if self.plot_option == 'inline':
scores.append(score)
epsilons.append(self.epsilon)
self._plot(frame_idx, scores, losses, epsilons)
elif self.plot_option == 'wandb':
wandb.log({
'Score': score,
'loss(10 frames avg)': np.mean(losses[-10:]),
'Epsilon': self.epsilon
})
print(score, end='\r')
else:
print(score, end='\r')
score = 0
state = self.get_init_state()
history_store = []
else:
state = next_state
self._epsilon_step()
print("Total training time: {}(hrs)".format(
(time.time() - tic) / 3600))
def _epsilon_step(self):
''' Epsilon decay control '''
eps_decay_init = 1 / 1200000
eps_decay = [
eps_decay_init, eps_decay_init / 2.5, eps_decay_init / 3.5,
eps_decay_init / 5.5
]
if self.epsilon > 0.35:
self.epsilon = max(self.epsilon - eps_decay[0], 0.1)
elif self.epsilon > 0.27:
self.epsilon = max(self.epsilon - eps_decay[1], 0.1)
elif self.epsilon > 1.7:
self.epsilon = max(self.epsilon - eps_decay[2], 0.1)
else:
self.epsilon = max(self.epsilon - eps_decay[3], 0.1)
def _compute_loss(self, batch: "Dictionary (S, A, R', S', Dones)"):
# If normalization is used, it must be applied to 'state' and 'next_state' here. ex) state/255
states = torch.FloatTensor(batch['states']).to(self.device) / 255
next_states = torch.FloatTensor(batch['next_states']).to(
self.device) / 255
actions = torch.LongTensor(batch['actions'].reshape(-1,
1)).to(self.device)
rewards = torch.FloatTensor(batch['rewards'].reshape(-1, 1)).to(
self.device)
dones = torch.FloatTensor(batch['dones'].reshape(-1,
1)).to(self.device)
current_q = self.q_current(states).gather(1, actions)
# The next line is the only difference from Vanila DQN.
next_q = self.q_target(next_states).gather(
1,
self.q_current(next_states).argmax(axis=1, keepdim=True)).detach()
mask = 1 - dones
target = (rewards + (mask * self.gamma * next_q)).to(self.device)
loss = F.smooth_l1_loss(current_q, target)
return loss
def _plot(self, frame_idx, scores, losses, epsilons):
clear_output(True)
plt.figure(figsize=(20, 5), facecolor='w')
plt.subplot(131)
plt.title('frame %s. score: %s' % (frame_idx, np.mean(scores[-10:])))
plt.plot(scores)
plt.subplot(132)
plt.title('loss')
plt.plot(losses)
plt.subplot(133)
plt.title('epsilons')
plt.plot(epsilons)
plt.show()
def train(game,
num_steps=60000000,
lr=0.00025,
gamma=0.99,
C=20000,
batch_size=32):
env = wrappers.wrap(gym.make(GAMES[game]))
num_actions = env.action_space.n
Q1 = QNetwork(num_actions)
Q2 = QNetwork(num_actions)
Q2.load_state_dict(Q1.state_dict())
if torch.cuda.is_available():
Q1.cuda()
Q2.cuda()
epsilon = Epsilon(1, 0.05, 1000000)
optimizer = torch.optim.Adam(Q1.parameters(), lr=lr)
optimizer.zero_grad()
state1 = env.reset()
t, last_t, loss, episode, score = 0, 0, 0, 0, 0
last_ts, scores = datetime.now(), collections.deque(maxlen=100)
while t < num_steps:
qvalues = Q1(state1)
if random() < epsilon(t):
action = env.action_space.sample()
else:
action = qvalues.data.max(dim=1)[1][0]
q = qvalues[0][action]
state2, reward, done, _info = env.step(action)
score += reward
if not done:
y = gamma * Q2(state2).detach().max(dim=1)[0][0] + reward
state1 = state2
else:
reward = FloatTensor([reward])
y = torch.autograd.Variable(reward, requires_grad=False)
state1 = env.reset()
scores.append(score)
score = 0
episode += 1
loss += torch.nn.functional.smooth_l1_loss(q, y)
t += 1
if done or t % batch_size == 0:
loss.backward()
optimizer.step()
optimizer.zero_grad()
loss = 0
if t % C == 0:
Q2.load_state_dict(Q1.state_dict())
torch.save(Q1.state_dict(), 'qlearning_{}.pt'.format(game))
if t % 1000 == 0:
ts = datetime.now()
datestr = ts.strftime('%Y-%m-%dT%H:%M:%S.%f')
avg = mean(scores) if scores else float('nan')
steps_per_sec = (t - last_t) / (ts - last_ts).total_seconds()
l = '{} step {} episode {} avg last 100 scores: {:.2f} ε: {:.2f}, steps/s: {:.0f}'
print(l.format(datestr, t, episode, avg, epsilon(t),
steps_per_sec))
last_t, last_ts = t, ts
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)
# 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=0.0, training_mode=True):
"""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)
if training_mode is True:
self.qnetwork_local.train()
# Epsilon-greedy action selection
if random.random() > eps:
action = np.argmax(action_values.cpu().data.numpy())
else:
action = random.choice(np.arange(self.action_size))
action = np.int32(action)
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 (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 DQN_Agent():
""" Interacts an learns from the environment. """
def __init__(self,
state_size,
action_size,
seed,
GAMMA=GAMMA,
TAU=TAU,
LR=LR,
UPDATE_EVERY=UPDATE_EVERY,
BUFFER_SIZE=BUFFER_SIZE,
BATCH_SIZE=BATCH_SIZE):
""" Initialize the agent.
==========
PARAMETERS
==========
state_size (int) = observation dimension of the environment
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.gamma = GAMMA
self.tau = TAU
self.lr = LR
self.update_every = UPDATE_EVERY
self.buffer_size = BUFFER_SIZE
self.batch_size = BATCH_SIZE
self.device = torch.device(
"cuda:0" if torch.cuda.is_available() else "cpu")
# instantiate online local and target network for weight updates
self.qnetwork_local = QNetwork(state_size, action_size,
seed).to(self.device)
self.qnetwork_target = QNetwork(state_size, action_size,
seed).to(self.device)
self.optimizer = optim.Adam(self.qnetwork_local.parameters(),
lr=self.lr)
# create a replay buffer
self.memory = ReplayBuffer(action_size, self.buffer_size,
self.batch_size, seed, self.device)
# time steps for updating target network every time t_step % 4 == 0
self.t_step = 0
def step(self, state, action, reward, next_state, done):
''' Append a SARS sequence to memory, then every update_every steps learn from experiences'''
self.memory.add(state, action, reward, next_state, done)
self.t_step = (self.t_step + 1) % self.update_every
if self.t_step == 0:
# in case enough samples are available in internal memory, sample and learn
if len(self.memory) > self.batch_size:
experiences = self.memory.sample()
self.learn(experiences, self.gamma)
def act(self, state, eps=0.):
""" Choose action from an epsilon-greedy policy
==========
PARAMETERS
==========
state (array) = current state space
eps (float) = epsilon, for epsilon-greedy action choice """
state = torch.from_numpy(state).float().unsqueeze(0).to(self.device)
self.qnetwork_local.eval()
with torch.no_grad():
action_values = self.qnetwork_local.forward(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):
""" Update the value parameters using experience tuples sampled from ReplayBuffer
==========
PARAMETERS
==========
experiences = Tuple of torch.Variable: SARS', done
gamma (float) = discount factor to weight rewards
"""
states, actions, rewards, next_states, dones = experiences
# calculate max predicted Q values for the next states using target model
Q_targets_next = self.qnetwork_target(next_states).detach().max(
1)[0].unsqueeze(1)
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# calculate expected Q vaues from the local model
Q_expected = self.qnetwork_local(states).gather(1, actions)
# compute MSE Loss
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, self.tau)
def soft_update(self, local_model, target_model, tau):
""" Soft update for model parameters, every update steps as defined above
theta_target = tau * theta_local + (1-tau)*theta_target
==========
PARAMETERS
==========
local_model, target_model = PyTorch Models, weights will be copied from-to
tau = interpolation parameter, type=float
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(self.tau * local_param.data +
(1.0 - self.tau) * target_param.data)
def train(env_name, seed=42, timesteps=1, epsilon_decay_last_step=1000,
er_capacity=1e4, batch_size=16, lr=1e-3, gamma=1.0, update_target=16,
exp_name='test', init_timesteps=100, save_every_steps=1e4, arch='nature',
dueling=False, play_steps=2, n_jobs=2):
"""
Main training function. Calls the subprocesses to get experience and
train the network.
"""
# Multiprocessing method
mp.set_start_method('spawn')
# Get PyTorch device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Set random seed for PyTorch
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
# Create logger
logger = Logger(exp_name, loggers=['tensorboard'])
# Create the Q network
_env = make_env(env_name, seed)
net = QNetwork(_env.observation_space, _env.action_space, arch=arch, dueling=dueling).to(device)
# Create the target network as a copy of the Q network
target_net = copy.deepcopy(net)
# Create buffer and optimizer
buffer = ExperienceReplay(capacity=int(er_capacity))
optimizer = optim.Adam(net.parameters(), lr=lr)
scheduler = StepLR(optimizer, step_size=LR_STEPS, gamma=0.99)
# Multiprocessing queue
obs_queue = mp.Queue(maxsize=n_jobs)
transition_queue = mp.Queue(maxsize=n_jobs)
workers, action_queues = [], []
for i in range(n_jobs):
action_queue = mp.Queue(maxsize=1)
_seed = seed + i * 1000
play_proc = mp.Process(target=play_func, args=(i, env_name, obs_queue, transition_queue, action_queue, _seed))
play_proc.start()
workers.append(play_proc)
action_queues.append(action_queue)
# Vars to keep track of performances and time
timestep = 0
current_reward, current_len = np.zeros(play_steps), np.zeros(play_steps, dtype=np.int64)
current_time = [time.time() for _ in range(play_steps)]
# Training loop
while timestep < timesteps:
# Compute the current epsilon
epsilon = EPSILON_STOP + max(0, (EPSILON_START - EPSILON_STOP)*(epsilon_decay_last_step-timestep)/epsilon_decay_last_step)
logger.log_kv('internals/epsilon', epsilon, timestep)
# Gather observation N_STEPS
ids, obs_batch = zip(*[obs_queue.get() for _ in range(play_steps)])
# Pre-process observation_batch for PyTorch
obs_batch = torch.from_numpy(np.array(obs_batch)).to(device)
# Select greedy action from policy, apply epsilon-greedy selection
greedy_actions = net(obs_batch).argmax(dim=1).cpu().detach().numpy()
probs = torch.rand(greedy_actions.shape)
actions = np.where(probs < epsilon, _env.action_space.sample(), greedy_actions)
# Send actions
for id, action in zip(ids, actions):
action_queues[id].put(action)
# Add transitions to experience replay
transitions = [transition_queue.get() for _ in range(play_steps)]
buffer.pushTransitions(transitions)
# Check if we need to update rewards, time and lengths
_, _, _, reward, done, _ = zip(*transitions)
current_reward += reward
current_len += 1
for i, done in enumerate(done):
if done:
# Log quantities
logger.log_kv('performance/return', current_reward[i], timestep)
logger.log_kv('performance/length', current_len[i], timestep)
logger.log_kv('performance/speed', current_len[i] / (time.time() - current_time[i]), timestep)
# Reset counters
current_reward[i] = 0.0
current_len[i] = 0
current_time[i] = time.time()
# Update number of steps
timestep += play_steps
# Check if we are in the warm-up phase, otherwise go on with policy update
if timestep < init_timesteps:
continue
# Learning rate upddate and log
scheduler.step()
logger.log_kv('internals/lr', scheduler.get_lr()[0], timestep)
# Clear grads
optimizer.zero_grad()
# Get a batch from experience replay
batch = buffer.sampleTransitions(batch_size)
def batch_preprocess(batch_item):
return torch.tensor(batch_item, dtype=(torch.long if isinstance(batch_item[0], np.int64) else None)).to(device)
ids, states_batch, actions_batch, rewards_batch, done_batch, next_states_batch = map(batch_preprocess, zip(*batch))
# Compute the loss function
state_action_values = net(states_batch).gather(1, actions_batch.unsqueeze(-1)).squeeze(-1)
next_state_values = target_net(next_states_batch).max(1)[0]
next_state_values[done_batch] = 0.0
expected_state_action_values = next_state_values.detach() * gamma + rewards_batch
loss = F.mse_loss(state_action_values, expected_state_action_values)
logger.log_kv('internals/loss', loss.item(), timestep)
loss.backward()
# Clip the gradients to avoid to abrupt changes (this is equivalent to Huber Loss)
for param in net.parameters():
param.grad.data.clamp_(-1, 1)
optimizer.step()
if timestep % update_target == 0:
target_net.load_state_dict(net.state_dict())
# Check if we need to save a checkpoint
if timestep % save_every_steps == 0:
torch.save(net.get_extended_state(), exp_name + '.pth')
# Ending
for i, worker in enumerate(workers):
action_queues[i].put(None)
worker.join()
def train(env_name,
arch,
timesteps=1,
init_timesteps=0,
seed=42,
er_capacity=1,
epsilon_start=1.0,
epsilon_stop=0.05,
epsilon_decay_stop=1,
batch_size=16,
target_sync=16,
lr=1e-3,
gamma=1.0,
dueling=False,
play_steps=1,
lr_steps=1e4,
lr_gamma=0.99,
save_steps=5e4,
logger=None,
experiment_name='test'):
"""
Main training function. Calls the subprocesses to get experience and
train the network.
"""
# Casting params which are expressable in scientific notation
def int_scientific(x):
return int(float(x))
timesteps, init_timesteps = map(int_scientific,
[timesteps, init_timesteps])
lr_steps, epsilon_decay_stop = map(int_scientific,
[lr_steps, epsilon_decay_stop])
er_capacity, target_sync, save_steps = map(
int_scientific, [er_capacity, target_sync, save_steps])
lr = float(lr)
# Multiprocessing method
mp.set_start_method('spawn')
# Get PyTorch device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Create the Q network
_env = make_env(env_name, seed)
net = QNetwork(_env.observation_space,
_env.action_space,
arch=arch,
dueling=dueling).to(device)
# Create the target network as a copy of the Q network
tgt_net = ptan.agent.TargetNet(net)
# Create buffer and optimizer
buffer = ptan.experience.ExperienceReplayBuffer(experience_source=None,
buffer_size=er_capacity)
optimizer = optim.Adam(net.parameters(), lr=lr)
scheduler = StepLR(optimizer, step_size=lr_steps, gamma=0.99)
# Multiprocessing queue
epsilon_schedule = (epsilon_start, epsilon_stop, epsilon_decay_stop)
exp_queue = mp.Queue(maxsize=play_steps * 2)
play_proc = mp.Process(target=play_func,
args=(env_name, net, exp_queue, seed, timesteps,
epsilon_schedule, gamma))
play_proc.start()
# Main training loop
timestep = 0
while play_proc.is_alive() and timestep < timesteps:
timestep += play_steps
# Query the environments and log results if the episode has ended
for _ in range(play_steps):
exp, info = exp_queue.get()
if exp is None:
play_proc.join()
break
buffer._add(exp)
logger.log_kv('internals/epsilon', info['epsilon'][0],
info['epsilon'][1])
if 'ep_reward' in info.keys():
logger.log_kv('performance/return', info['ep_reward'],
timestep)
logger.log_kv('performance/length', info['ep_length'],
timestep)
logger.log_kv('performance/speed', info['speed'], timestep)
# Check if we are in the starting phase
if len(buffer) < init_timesteps:
continue
scheduler.step()
logger.log_kv('internals/lr', scheduler.get_lr()[0], timestep)
# Get a batch from experience replay
optimizer.zero_grad()
batch = buffer.sample(batch_size * play_steps)
# Unpack the batch
states, actions, rewards, dones, next_states = unpack_batch(batch)
states_v = torch.tensor(states).to(device)
next_states_v = torch.tensor(next_states).to(device)
actions_v = torch.tensor(actions).to(device)
rewards_v = torch.tensor(rewards).to(device)
done_mask = torch.ByteTensor(dones).to(device)
# Optimize defining the loss function
state_action_values = net(states_v).gather(
1, actions_v.unsqueeze(-1)).squeeze(-1)
next_state_values = tgt_net.target_model(next_states_v).max(1)[0]
next_state_values[done_mask] = 0.0
expected_state_action_values = next_state_values.detach(
) * gamma + rewards_v
loss = F.mse_loss(state_action_values, expected_state_action_values)
logger.log_kv('internals/loss', loss.item(), timestep)
loss.backward()
# Clip the gradients to avoid to abrupt changes (this is equivalent to Huber Loss)
for param in net.parameters():
param.grad.data.clamp_(-1, 1)
optimizer.step()
# Check if the target network need to be synched
if timestep % target_sync == 0:
tgt_net.sync()
# Check if we need to save a checkpoint
if timestep % save_steps == 0:
torch.save(net.get_extended_state(), experiment_name + '.pth')
