We re-formulate solving a reinforcement learning task as synthesizing a task-solving program that can be executed to interact with the environment and maximize the return. We first learn a program embedding space that continuously parameterizes a diverse set of programs sampled from a program dataset. Then, we train a meta-policy, whose action space is the learned program embedding space, to produce a series of programs (i.e., predict a series of actions) to yield a composed task-solving program.