Task: Summarization
Input (English): Many deep reinforcement learning approaches use graphical state representations,
 this means visually distinct games that share the same underlying structure cannot
 effectively share knowledge. This paper outlines a new approach for learning
 underlying game state embeddings irrespective of the visual rendering of the game
 state. We utilise approaches from multi-task learning and domain adaption in
 order to place visually distinct game states on a shared embedding manifold. We
 present our results in the context of deep reinforcement learning agents.
Output (English): This paper discusses an agent architecture which uses a shared representation to train multiple tasks with different sprite level visual statistics