def baseline_optimizer_arguments(self, states, internals, reward):
        """
        Returns the baseline optimizer arguments including the time, the list of variables to  
        optimize, and various functions which the optimizer might require to perform an update  
        step.

        Args:
            states: Dict of state tensors.
            internals: List of prior internal state tensors.
            reward: Reward tensor.

        Returns:
            Baseline optimizer arguments as dict.
        """
        arguments = dict(
            time=self.global_timestep,
            variables=self.baseline.get_variables(),
            arguments=dict(
                states=states,
                internals=internals,
                reward=reward,
                update=tf.constant(value=True),
            ),
            fn_reference=self.baseline.reference,
            fn_loss=self.fn_baseline_loss,
            # source_variables=self.network.get_variables()
        )
        if self.global_model is not None:
            arguments['global_variables'] = self.global_model.baseline.get_variables()
        return arguments
def baseline_optimizer_arguments(self, states, internals, reward):
        """
        Returns the baseline optimizer arguments including the time, the list of variables to  
        optimize, and various functions which the optimizer might require to perform an update  
        step.

        Args:
            states: Dict of state tensors.
            internals: List of prior internal state tensors.
            reward: Reward tensor.

        Returns:
            Baseline optimizer arguments as dict.
        """
        arguments = dict(
            time=self.global_timestep,
            variables=self.baseline.get_variables(),
            arguments=dict(
                states=states,
                internals=internals,
                reward=reward,
                update=tf.constant(value=True),
            ),
            fn_reference=self.baseline.reference,
            fn_loss=self.fn_baseline_loss,
            # source_variables=self.network.get_variables()
        )
        if self.global_model is not None:
            arguments['global_variables'] = self.global_model.baseline.get_variables()
        return arguments
def baseline_optimizer_arguments ( self , states , internals , reward ) : arguments = dict ( time = self . global_timestep , variables = self . baseline . get_variables ( ) , arguments = dict ( states = states , internals = internals , reward = reward , update = tf . constant ( value = True ) , ) , fn_reference = self . baseline . reference , fn_loss = self . fn_baseline_loss , # source_variables=self.network.get_variables() ) if self . global_model is not None : arguments [ 'global_variables' ] = self . global_model . baseline . get_variables ( ) return arguments
Returns the baseline optimizer arguments including the time, the list of variables to  
        optimize, and various functions which the optimizer might require to perform an update  
        step.

        Args:
            states: Dict of state tensors.
            internals: List of prior internal state tensors.
            reward: Reward tensor.

        Returns:
            Baseline optimizer arguments as dict.
Returns the baseline optimizer arguments including the time the list of variables to optimize and various functions which the optimizer might require to perform an update step .