  Reinforcement learning is concerned with identifying reward-maximizing
behaviour policies in environments that are initially unknown. State-of-the-art
reinforcement learning approaches, such as deep Q-networks, are model-free and
learn to act effectively across a wide range of environments such as Atari
games, but require huge amounts of data. Model-based techniques are more
data-efficient, but need to acquire explicit knowledge about the environment.
  In this paper, we take a step towards using model-based techniques in
environments with a high-dimensional visual state space by demonstrating that
it is possible to learn system dynamics and the reward structure jointly. Our
contribution is to extend a recently developed deep neural network for video
frame prediction in Atari games to enable reward prediction as well. To this
end, we phrase a joint optimization problem for minimizing both video frame and
reward reconstruction loss, and adapt network parameters accordingly. Empirical
evaluations on five Atari games demonstrate accurate cumulative reward
prediction of up to 200 frames. We consider these results as opening up
important directions for model-based reinforcement learning in complex,
initially unknown environments.
