title: DEEP ADVERSARIAL FORWARD MODEL
abstract: Learning world dynamics has recently been investigated as a way to make reinforcement
  learning (RL) algorithms to be more sample efficient and interpretable.
  In this paper, we propose to capture an environment dynamics with a novel forward
  model that leverages recent works on adversarial learning and visual control. Such
  a model estimates future observations conditioned on the current ones and other
  input variables such as actions taken by an RL-agent. We focus on image generation
  which is a particularly challenging topic but our method can be adapted to
  other modalities. More precisely, our forward model is trained to produce realistic
  observations of the future while a discriminator model is trained to distinguish
  between real images and the model’s prediction of the future. This approach overcomes
  the need to define an explicit loss function for the forward model which is currently
  used for solving such a class of problem. As a consequence, our learning protocol
  does not have to rely on an explicit distance such as Euclidean distance which
  tends to produce unsatisfactory predictions. To illustrate our method, empirical
  qualitative and quantitative results are presented on a real driving scenario, along
  with qualitative results on Atari game Frostbite.
authors: Morgan Funtowicz Tomi Silander Arnaud Sors Julien Perez
hIndex: Tomi Silander 21