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.
An approach to learning a shared embedding space between visually distinct games. A new approach for learning underlying structure of visually distinct games combining convolutional layers for processing input images, Asynchronous Advantage Actor Critic for deep reinforcement learning and adversarial approach to force the embedding representation to be independent of the visual representation of games Introduces a method to learn a policy on visually distinct games by adapting deep reinforcement learning. This paper discusses an agent architecture which uses a shared representation to train multiple tasks with different sprite level visual statistics