Planning with Pixels in (Almost) Real Time
  Recently, width-based planning methods have been shown to yield
state-of-the-art results in the Atari 2600 video games. For this, the states
were associated with the (RAM) memory states of the simulator. In this work, we
consider the same planning problem but using the screen instead. By using the
same visual inputs, the planning results can be compared with those of humans
and learning methods. We show that the planning approach, out of the box and
without training, results in scores that compare well with those obtained by
humans and learning methods, and moreover, by developing an episodic, rollout
version of the IW(k) algorithm, we show that such scores can be obtained in
almost real time.
