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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 Submissionreaders: everyoneShow 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.