class Simulator(object):
def __init__(self):
super(Simulator, self).__init__()
self.model = cfg.model(cfg)
self.optim = cfg.optim(cfg.learning_rate)
self.loss = cfg.loss
self.epoch = tf.Variable(0)
self.writer = Writer(cfg)
# Restore if save exists
if Path('./simulator_saves/best').is_dir():
self.model, self.optim, self.epoch = self.writer.restore(
model=self.model, optim=self.optim, epoch=self.epoch)
self.preprocessing()
def preprocessing(self):
if cfg.package_data or not Path('./data.pkl').is_file():
vizdoom = VizDoom(cfg)
memory = []
for episode in trange(cfg.gather_epochs):
vizdoom.new_episode()
s0 = vizdoom.get_preprocessed_state()
while not vizdoom.is_episode_finished():
action = random.choice(cfg.actions)
vizdoom.make_action(action)
s1 = vizdoom.get_preprocessed_state()
action = np.reshape(
action, [1, 1, len(cfg.actions)]).astype(np.float32)
memory.append([s0, action, s1])
s0 = s1
with open('data.pkl', 'wb') as f:
pickle.dump(memory, f)
# Load data
with open(cfg.data_dir, 'rb') as f:
s0, action, s1 = zip(*pickle.load(f))
self.size = len(s0)
self.data = tf.data.Dataset.from_tensor_slices(
(np.array(s0), np.array(action), np.array(s1)))
def update(self, s0, action, s1):
# Normalize
s0_n = tf.image.per_image_standardization(s0)
truth = tf.image.per_image_standardization(s1) - s0_n
# Construct graph
with tf.GradientTape() as tape:
# Approximate next frame
logits = self.model(s0_n, action)
# Compare generated transformation matrix with truth
loss = tf.reduce_mean(self.loss(truth, logits))
# Log stats, images
self.writer.log(self.optim, tape, loss)
self.writer.log_state("logits", logits)
self.writer.log_state("truth_logits", truth)
# Compute/apply gradients
grads = tape.gradient(loss, self.model.trainable_weights)
grads_and_vars = zip(grads, self.model.trainable_weights)
self.optim.apply_gradients(grads_and_vars)
self.writer.global_step.assign_add(1)
def train(self):
for epoch in trange(self.epoch.numpy(), cfg.epochs):
# Uniform shuffle
batch = self.data.shuffle(self.size).batch(cfg.batch_size)
for s0, action, s1 in batch:
self.update(s0, action, s1)
self.epoch.assign_add(1)
self.writer.save(self.model, self.optim, self.epoch)
def predict(self, s0, action):
s0_n = tf.image.per_image_standardization(s0)
logits = self.model(s0_n, action[None])
return logits + s0_n
class AlphaDoom(object):
def __init__(self):
super(AlphaDoom, self).__init__()
self.mcts = MCTS(cfg)
self.replay = Replay(cfg)
self.autoencoder = Simulator()
self.autoencoder.train()
#self.autoencoder = AutoEncoder()
#tf.train.Checkpoint(model=self.autoencoder).restore(tf.train.latest_checkpoint('./simulator_saves/best'))
# Load selected model
self.model = cfg.model(cfg)
self.loss1 = cfg.loss1
self.loss2 = cfg.loss2
self.optim = cfg.optim(cfg.learning_rate)
self.epoch = tf.Variable(0)
self.writer = Writer(cfg)
# Restore if save exists
if Path('./alphadoom_saves/best').is_dir():
self.model, self.optim, self.epoch = self.writer.restore(
self.model, self.optim, self.epoch)
self.vizdoom = VizDoom(cfg)
def update(self):
# Fetch batch of experiences
s0, pi, z = self.replay.fetch()
z = np.array(z).reshape((len(z), 1))
pi = np.array(pi, dtype=np.float32)
# Construct graph
with tf.GradientTape() as tape:
p, v = self.model(s0)
loss1 = self.loss1(z, v)
loss2 = self.loss2(pi, p)
l2_reg = tf.add_n([tf.nn.l2_loss(v) for v in self.model.weights])
loss = loss1 + loss2 + cfg.c * l2_reg
# Log stats
self.writer.log(self.optim, tape, loss)
self.writer.log_var("MSE", loss1)
self.writer.log_var("Cross Entropy", loss2)
self.writer.log_var("reg", l2_reg)
# Compute/apply gradients
grads = tape.gradient(loss, self.model.weights)
grads_and_vars = zip(grads, self.model.weights)
self.optim.apply_gradients(grads_and_vars)
self.writer.global_step.assign_add(1)
# Runs N simulations, where each sim reaches a leaf node in MCTS tree
def simulate(self):
for i in range(cfg.num_sims):
# Find leaf
leaf = self.mcts.search()
# Simulate leaf's state
action = np.reshape(leaf.a,
[1, 1, len(cfg.actions)]).astype(np.float32)
leaf.s = self.autoencoder.predict(leaf.parent.s[-1][None], action)
# Get p, the prior probability set of all actions (edges) from current leaf node, and v, the value of current leaf node
s = tf.concat(leaf.s, axis=-1)
p, v = self.model(s)
# Backprop through MCTS tree
self.mcts.update(leaf, v, p)
# Returns best action
def perform_action(self, frames):
# Shape (H, W, 1) to (1, H, W, 1)
self.mcts.root.s = frames
self.simulate()
action = self.mcts.select_action()
# Take action
reward = self.vizdoom.make_action(action)
return action, reward
def train(self):
if Path('/replay.pkl').is_file():
with open('/replay.pkl', 'rb') as f:
self.replay.memory = pickle.load(f)
for epoch in trange(self.epoch.numpy(), cfg.epochs):
self.vizdoom.new_episode()
frame = self.vizdoom.get_preprocessed_state()
frames = []
# Init stack of n frames
for i in range(cfg.num_frames):
frames.append(frame)
z = 0
memories = []
while not self.vizdoom.is_episode_finished():
pi, reward = self.perform_action(frames)
# Check final outcome; +1 to states if positive reward, -1 if negative
if reward >= 0:
z = 1
break
else:
z = -1
if self.vizdoom.is_episode_finished == True:
break
# Update frames with latest image
frames.pop(0)
frames.append(self.vizdoom.get_preprocessed_state())
s0 = tf.concat(frames, axis=-1)
memories.append([s0, pi])
self.writer.log_var("z", z)
# Add memories to experience replay
for i in range(len(memories)):
memories[i].append(z)
self.replay.push(memories[i])
# Train on experiences from memory
self.update()
# Save model
if epoch % cfg.save_freq == 0:
self.writer.save(self.model, self.optim, self.epoch)
with open('./replay.pkl', 'wb') as f:
pickle.dump(self.replay.memory, f)
self.writer.save(self.model, self.optim, self.epoch)
