a4c4a4f2d33081806397784f96b113d5b3b32c9a,tensorforce/core/memories/prioritized_replay.py,PrioritizedReplay,tf_retrieve_timesteps,#PrioritizedReplay#Any#,149

Before Change

            return tf.no_op()

    def tf_retrieve_timesteps(self, n):
        num_timesteps = (self.memory_index - self.episode_indices[0] - 2) % self.capacity + 1
        n = tf.minimum(x=n, y=num_timesteps)

        indices = tf.zeros(shape=(n,), dtype=tf.int32)

After Change

        // Vectorized sampling.
        sum_priorities = tf.reduce_sum(input_tensor=self.priorities, axis=0)
        sample = tf.random_uniform(shape=(num_priority_elements,),dtype=tf.float32)
        indices = tf.constant(value=0, shape=(num_priority_elements,))

        def cond(loop_index, sample, priorities):
            return tf.reduce_all(input_tensor=(sample <= 0))

        def sampling_fn(loop_index, sample, priorities):
            priority = self.priorities[loop_index]
            sample -= priority / priorities

            return (tf.add(loop_index, 1), sample, priorities)

        priority_indices = tf.while_loop(
            cond=cond,
            body=sampling_fn,
            loop_vars=(indices, sample, sum_priorities)
        )[0]

        priority_terminal = tf.gather(params=self.terminal_memory, indices=priority_indices)
        priority_indices = tf.boolean_mask(tensor=indices, mask=tf.logical_not(x=priority_terminal))

Instances