Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games

Dino S. Ratcliffe, Luca Citi, Sam Devlin, Udo Kruschwitz

Feb 15, 2018 (modified: Feb 15, 2018) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • 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

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