  Deeply-learned planning methods are often based on learning representations
that are optimized for unrelated tasks. For example, they might be trained on
reconstructing the environment. These representations are then combined with
predictor functions for simulating rollouts to navigate the environment. We
find this principle of learning representations unsatisfying and propose to
learn them such that they are directly optimized for the task at hand: to be
maximally predictable for the predictor function. This results in
representations that are by design optimal for the downstream task of planning,
where the learned predictor function is used as a forward model.
  To this end, we propose a new way of jointly learning this representation
along with the prediction function, a system we dub Latent Representation
Prediction Network (LARP). The prediction function is used as a forward model
for search on a graph in a viewpoint-matching task and the representation
learned to maximize predictability is found to outperform a pre-trained
representation. Our approach is shown to be more sample-efficient than standard
reinforcement learning methods and our learned representation transfers
successfully to dissimilar objects.
