  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.
