452cf41746dd7c4572b6e6766185431bce7f5ee1,tf_agents/agents/ddpg/critic_rnn_network.py,CriticRnnNetwork,call,#CriticRnnNetwork#Any#Any#Any#Any#,154

Before Change

    joint = batch_squash.unflatten(joint)  // [B x T, ...] -> [B, T, ...]

    with tf.name_scope("reset_mask"):
      reset_mask = tf.equal(step_type, time_step.StepType.FIRST)
    // Unroll over the time sequence.
    joint, network_state = self._dynamic_unroll(
        joint,
        reset_mask,
        initial_state=network_state,

After Change

        initial_state=network_state,
        training=training,
        **network_kwargs)
    if isinstance(self._lstm_network, dynamic_unroll_layer.DynamicUnroll):
      joint, network_state = output
    else:
      joint = output[0]
      network_state = tf.nest.pack_sequence_as(
          self._lstm_network.cell.state_size, tf.nest.flatten(output[1:]))

    output = batch_squash.flatten(joint)  // [B, T, ...] -> [B x T, ...]

    for layer in self._output_layers:
      output = layer(output, training=training)

Instances