Abstract
Text-based adventure games provide a platform on which to explore
reinforcement learning in the context of a combinatorial action space, such as
natural language. We present a deep reinforcement learning architecture that
represents the game state as a knowledge graph which is learned during
exploration. This graph is used to prune the action space, enabling more
efficient exploration. The question of which action to take can be reduced to a
question-answering task, a form of transfer learning that pre-trains certain
parts of our architecture. In experiments using the TextWorld framework, we
show that our proposed technique can learn a control policy faster than
baseline alternatives. We have also open-sourced our code at
https://github.com/rajammanabrolu/KG-DQN.
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