tensorforce/models/q_model.py
def tf_q_delta(self, q_value, next_q_value, terminal, reward):
        """
        Creates the deltas (or advantage) of the Q values.

        :return: A list of deltas per action
        """
        for _ in range(util.rank(q_value) - 1):
            terminal = tf.expand_dims(input=terminal, axis=1)
            reward = tf.expand_dims(input=reward, axis=1)

        multiples = (1,) + util.shape(q_value)[1:]
        terminal = tf.tile(input=terminal, multiples=multiples)
        reward = tf.tile(input=reward, multiples=multiples)

        zeros = tf.zeros_like(tensor=next_q_value)
        next_q_value = tf.where(condition=terminal, x=zeros, y=(self.discount * next_q_value))

        return reward + next_q_value - q_value
def tf_q_delta(self, q_value, next_q_value, terminal, reward):
        """
        Creates the deltas (or advantage) of the Q values.

        :return: A list of deltas per action
        """
        for _ in range(util.rank(q_value) - 1):
            terminal = tf.expand_dims(input=terminal, axis=1)
            reward = tf.expand_dims(input=reward, axis=1)

        multiples = (1,) + util.shape(q_value)[1:]
        terminal = tf.tile(input=terminal, multiples=multiples)
        reward = tf.tile(input=reward, multiples=multiples)

        zeros = tf.zeros_like(tensor=next_q_value)
        next_q_value = tf.where(condition=terminal, x=zeros, y=(self.discount * next_q_value))

        return reward + next_q_value - q_value
Creates the deltas (or advantage) of the Q values.

        :return: A list of deltas per action