  In many vision-based reinforcement learning (RL) problems, the agent controls
a movable object in its visual field, e.g., the player's avatar in video games
and the robotic arm in visual grasping and manipulation. Leveraging
action-conditioned video prediction, we propose an end-to-end learning
framework to disentangle the controllable object from the observation signal.
The disentangled representation is shown to be useful for RL as additional
observation channels to the agent. Experiments on a set of Atari games with the
popular Double DQN algorithm demonstrate improved sample efficiency and game
performance (from 222.8% to 261.4% measured in normalized game scores, with
prediction bonus reward).
