- Abstract: 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.
- TL;DR: An approach to learning a shared embedding space between visually distinct games.
- Keywords: Deep Reinforcement Learning, Domain Adaptation, Adversarial Networks