Definition: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Input: 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.

Introduces a method to learn a policy on visually distinct games by adapting deep reinforcement learning.