update_weights (https://arxiv.org/src/1911.08265v1/anc/pseudocode.py)

pseudocode.py

def update_weights(optimizer: tf.train.Optimizer, network: Network, batch,
                   weight_decay: float):
  loss = 0
  for image, actions, targets in batch:
    # Initial step, from the real observation.
    value, reward, policy_logits, hidden_state = network.initial_inference(
        image)
    predictions = [(1.0, value, reward, policy_logits)]

    # Recurrent steps, from action and previous hidden state.
    for action in actions:
      value, reward, policy_logits, hidden_state = network.recurrent_inference(
          hidden_state, action)
      predictions.append((1.0 / len(actions), value, reward, policy_logits))

      hidden_state = tf.scale_gradient(hidden_state, 0.5)

    for prediction, target in zip(predictions, targets):
      gradient_scale, value, reward, policy_logits = prediction
      target_value, target_reward, target_policy = target

      l = (
          scalar_loss(value, target_value) +
          scalar_loss(reward, target_reward) +
          tf.nn.softmax_cross_entropy_with_logits(
              logits=policy_logits, labels=target_policy))

      loss += tf.scale_gradient(l, gradient_scale)

  for weights in network.get_weights():
    loss += weight_decay * tf.nn.l2_loss(weights)

  optimizer.minimize(loss)