The paper introduces a hierarchical reinforcement learning framework called Feudal Networks (FuN) that enables agents to learn a hierarchy of temporal abstractions. The proposed model consists of a Manager and a Worker, with the Manager learning high-level policies and the Worker learning low-level policies. The approach is demonstrated to achieve state-of-the-art performance on a range of challenging tasks in the Atari domain.