class Leaner:
def __init__(self, config: MuZeroConfig, storage: SharedStorage,
replay_buffer: ReplayBuffer):
self.config = config
self.storage = storage
self.replay_buffer = replay_buffer
self.summary = create_summary(name="leaner")
self.metrics_loss = Mean(f'leaner-loss', dtype=tf.float32)
self.network = Network(self.config)
self.lr_schedule = ExponentialDecay(
initial_learning_rate=self.config.lr_init,
decay_steps=self.config.lr_decay_steps,
decay_rate=self.config.lr_decay_rate)
self.optimizer = Adam(learning_rate=self.lr_schedule)
def start(self):
while self.network.training_steps() < self.config.training_steps:
if ray.get(self.replay_buffer.size.remote()) > 0:
self.train()
if self.network.training_steps(
) % self.config.checkpoint_interval == 0:
weigths = self.network.get_weights()
self.storage.update_network.remote(weigths)
if self.network.training_steps(
) % self.config.save_interval == 0:
self.network.save()
print("Finished")
def train(self):
batch = ray.get(self.replay_buffer.sample_batch.remote())
with tf.GradientTape() as tape:
loss = self.network.loss_function(batch)
grads = tape.gradient(loss, self.network.get_variables())
self.optimizer.apply_gradients(zip(grads,
self.network.get_variables()))
self.metrics_loss(loss)
with self.summary.as_default():
tf.summary.scalar(f'loss', self.metrics_loss.result(),
self.network.training_steps())
self.metrics_loss.reset_states()
self.network.update_training_steps()
class SharedStorage(object):
def __init__(self, config: MuZeroConfig):
self.config = config
self.network = Network(self.config)
self._started = False
def get_network_weights(self):
return self.network.get_weights()
def update_network(self, weights):
self.network.set_weights(weights)
if not self._started:
self._started = True
def started(self):
return self._started
