Value Explicit Pretraining for Goal-Based Transfer Learning

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.