tensorforce/models/memory_model.py
def tf_optimization(self, states, internals, actions, terminal, reward, next_states=None, next_internals=None):
        """
        Creates the TensorFlow operations for performing an optimization update step based
        on the given input states and actions batch.

        Args:
            states: Dict of state tensors.
            internals: List of prior internal state tensors.
            actions: Dict of action tensors.
            terminal: Terminal boolean tensor.
            reward: Reward tensor.
            next_states: Dict of successor state tensors.
            next_internals: List of posterior internal state tensors.

        Returns:
            The optimization operation.
        """
        arguments = self.optimizer_arguments(
            states=states,
            internals=internals,
            actions=actions,
            terminal=terminal,
            reward=reward,
            next_states=next_states,
            next_internals=next_internals
        )
        return self.optimizer.minimize(**arguments)
def tf_optimization(self, states, internals, actions, terminal, reward, next_states=None, next_internals=None):
        """
        Creates the TensorFlow operations for performing an optimization update step based
        on the given input states and actions batch.

        Args:
            states: Dict of state tensors.
            internals: List of prior internal state tensors.
            actions: Dict of action tensors.
            terminal: Terminal boolean tensor.
            reward: Reward tensor.
            next_states: Dict of successor state tensors.
            next_internals: List of posterior internal state tensors.

        Returns:
            The optimization operation.
        """
        arguments = self.optimizer_arguments(
            states=states,
            internals=internals,
            actions=actions,
            terminal=terminal,
            reward=reward,
            next_states=next_states,
            next_internals=next_internals
        )
        return self.optimizer.minimize(**arguments)
def tf_optimization ( self , states , internals , actions , terminal , reward , next_states = None , next_internals = None ) : arguments = self . optimizer_arguments ( states = states , internals = internals , actions = actions , terminal = terminal , reward = reward , next_states = next_states , next_internals = next_internals ) return self . optimizer . minimize ( * * arguments )
Creates the TensorFlow operations for performing an optimization update step based
        on the given input states and actions batch.

        Args:
            states: Dict of state tensors.
            internals: List of prior internal state tensors.
            actions: Dict of action tensors.
            terminal: Terminal boolean tensor.
            reward: Reward tensor.
            next_states: Dict of successor state tensors.
            next_internals: List of posterior internal state tensors.

        Returns:
            The optimization operation.
Creates the TensorFlow operations for performing an optimization update step based on the given input states and actions batch .