title: Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games
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.
authors: Dino S. Ratcliffe Luca Citi Sam Devlin Udo Kruschwitz
hIndex: Luca Citi 28