Title: Anticipatory Asynchronous Advantage Actor-Critic (A4C): The power of Anticipation in Deep Reinforcement Learning. Abstract: We propose to extend existing deep reinforcement learning  (Deep RL) algorithms by allowing them to additionally choose sequences of actions as a part of their policy.  This modification forces the network to anticipate the reward of action sequences, which, as we show, improves the exploration leading to better convergence. Our proposal is simple, flexible, and can be easily incorporated into any Deep RL framework. We show the power of our scheme by consistently outperforming the state-of-the-art GA3C algorithm on several popular Atari Games.