  We propose a method that allows for learning task-agnostic representations
based on value function estimates from a sequence of observations where the
last frame corresponds to a goal. These representations would learn to relate
states across different tasks, based on the temporal distance to the goal
state, irrespective of the appearance changes and dynamics. This method could
be used to transfer learnt policies/skills to unseen related tasks.

Machine Learning;Robotics