c2c9dddf6d99bfa14b8ffb65b507a6be50b0ad6e,tensorforce/models/pg_model.py,PGModel,tf_optimization,#PGModel#Any#Any#Any#Any#Any#Any#Any#,197

Before Change

                    return loss + regularization_loss

        // TODO: time as argument?
        baseline_optimization = self.baseline_optimizer.minimize(
            time=self.timestep,
            variables=self.baseline.get_variables(),
            arguments=dict(
                states=states,
                internals=internals,
                reward=cumulative_reward,
                update=tf.constant(value=True),
            ),
            fn_reference=self.baseline.reference,
            fn_loss=fn_loss,
            source_variables=self.network.get_variables()
        )

        return tf.group(optimization, baseline_optimization)

    def get_variables(self, include_non_trainable=False):

After Change

            next_internals=next_internals
        )

        if self.baseline_optimizer is not None:
            cumulative_reward = self.fn_discounted_cumulative_reward(terminal=terminal, reward=reward, discount=self.discount)

            arguments = self.baseline_optimizer_arguments(
                states=states,
                internals=internals,
                reward=cumulative_reward,
            )
            baseline_optimization = self.baseline_optimizer.minimize(**arguments)

            optimization = tf.group(optimization, baseline_optimization)

        return optimization

    def get_variables(self, include_submodules=False, include_nontrainable=False):
        model_variables = super(PGModel, self).get_variables(

Instances