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• W2911820093 abstract "We introduce Act2Vec, a general framework for learning context-based action representation for Reinforcement Learning. Representing actions in a vector space help reinforcement learning algorithms achieve better performance by grouping similar actions and utilizing relations between different actions. We show how prior knowledge of an environment can be extracted from demonstrations and injected into action vector representations that encode natural compatible behavior. We then use these for augmenting state representations as well as improving function approximation of Q-values. We visualize and test action embeddings in three domains including a drawing task, a high dimensional navigation task, and the large action space domain of StarCraft II." @default.
• W2911820093 created "2019-02-21" @default.
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• W2911820093 date "2019-02-04" @default.
• W2911820093 modified "2023-09-27" @default.
• W2911820093 title "The Natural Language of Actions" @default.
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