  Can we learn policies in reinforcement learning without rewards? Can we learn
a policy just by trying to reach a goal state? We answer these questions
positively by proposing a multi-step procedure that first learns a world model
that goes backward in time, secondly generates goal-reaching backward
trajectories, thirdly improves those sequences using shortest path finding
algorithms, and finally trains a neural network policy by imitation learning.
We evaluate our method on a deterministic maze environment where the
observations are $64\times 64$ pixel bird's eye images and can show that it
consistently reaches several goals.
