tensorforce/models/model.py
def tf_action_exploration(self, action, exploration, action_spec):
        """
        Applies optional exploration to the action (post-processor for action outputs).

        Args:
             action (tf.Tensor): The original output action tensor (to be post-processed).
             exploration (Exploration): The Exploration object to use.
             action_spec (dict): Dict specifying the action space.
        Returns:
            The post-processed action output tensor.
        """
        action_shape = tf.shape(input=action)
        exploration_value = exploration.tf_explore(
            episode=self.global_episode,
            timestep=self.global_timestep,
            shape=action_spec['shape']
        )
        exploration_value = tf.expand_dims(input=exploration_value, axis=0)

        if action_spec['type'] == 'bool':
            action = tf.where(
                condition=(tf.random_uniform(shape=action_shape) < exploration_value),
                x=(tf.random_uniform(shape=action_shape) < 0.5),
                y=action
            )

        elif action_spec['type'] == 'int':
            action = tf.where(
                condition=(tf.random_uniform(shape=action_shape) < exploration_value),
                x=tf.random_uniform(shape=action_shape, maxval=action_spec['num_actions'], dtype=util.tf_dtype('int')),
                y=action
            )

        elif action_spec['type'] == 'float':
            noise = tf.random_normal(shape=action_shape, dtype=util.tf_dtype('float'))
            action += noise * exploration_value
            if 'min_value' in action_spec:
                action = tf.clip_by_value(
                    t=action,
                    clip_value_min=action_spec['min_value'],
                    clip_value_max=action_spec['max_value']
                )

        return action
def tf_action_exploration(self, action, exploration, action_spec):
        """
        Applies optional exploration to the action (post-processor for action outputs).

        Args:
             action (tf.Tensor): The original output action tensor (to be post-processed).
             exploration (Exploration): The Exploration object to use.
             action_spec (dict): Dict specifying the action space.
        Returns:
            The post-processed action output tensor.
        """
        action_shape = tf.shape(input=action)
        exploration_value = exploration.tf_explore(
            episode=self.global_episode,
            timestep=self.global_timestep,
            shape=action_spec['shape']
        )
        exploration_value = tf.expand_dims(input=exploration_value, axis=0)

        if action_spec['type'] == 'bool':
            action = tf.where(
                condition=(tf.random_uniform(shape=action_shape) < exploration_value),
                x=(tf.random_uniform(shape=action_shape) < 0.5),
                y=action
            )

        elif action_spec['type'] == 'int':
            action = tf.where(
                condition=(tf.random_uniform(shape=action_shape) < exploration_value),
                x=tf.random_uniform(shape=action_shape, maxval=action_spec['num_actions'], dtype=util.tf_dtype('int')),
                y=action
            )

        elif action_spec['type'] == 'float':
            noise = tf.random_normal(shape=action_shape, dtype=util.tf_dtype('float'))
            action += noise * exploration_value
            if 'min_value' in action_spec:
                action = tf.clip_by_value(
                    t=action,
                    clip_value_min=action_spec['min_value'],
                    clip_value_max=action_spec['max_value']
                )

        return action
Applies optional exploration to the action (post-processor for action outputs).

        Args:
             action (tf.Tensor): The original output action tensor (to be post-processed).
             exploration (Exploration): The Exploration object to use.
             action_spec (dict): Dict specifying the action space.
        Returns:
            The post-processed action output tensor.