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.