Knowledge graph seeding, question-answering pretraining, and source-to-target network initialization improve deep Q-learning agents on text-adventure games compared to training from scratch.
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Transfer in Deep Reinforcement Learning using Knowledge Graphs
Knowledge graph seeding, question-answering pretraining, and source-to-target network initialization improve deep Q-learning agents on text-adventure games compared to training from scratch.