Bipedal locomotion presents a challenging set of control tasks that can be de- composed as (1) primitive and (2) task-specific high-level tasks. There exists a diverse set of data-driven and model-based controller optimization methods that enable legged robots to perform well on primitive (low-level) tasks such as walking, standing, and jumping. Learning high-level legged locomotion tasks is generally modeled as a two-layer optimization problem with a high-level and low- level control policy. We propose a similar and simple framework that uses Deep Reinforcement Learning (RL) to learn a high-level task given a fixed and high- performing low-level policy. State-of-the-art architectures are developed as end- to-end frameworks where both the parameters of low-level and high-level policies are optimized jointly during training. Our method decouples the learning procedure of the high and low-level tasks as disjoint optimization problems, and uses a curriculum learning based approach to optimize the high-level task. We demonstrate the learning framework by teaching Cassie, a bipedal robot, to dodge a rolling ball using a jumping and standing primitive controller.