Dialogue policy learning (DPL) is a key component in a task-oriented dialogue (TOD) system. Its goal is to decide the next action of the dialogue system, given the dialogue state at each turn based on a learned dialogue policy. Reinforcement learning (RL) is widely used to optimize this dialogue policy. In the learning process, the user is regarded as the environment and the system as the agent. In this paper, we present an overview of the recent advances and challenges in dialogue policy from the perspective of RL. More specific ally, we identify the problems and summarize corresponding solutions for RL-based dialogue policy learning. In addition, we provide a comprehensive survey of applying RL to DPL by categorizing recent methods into five basic elements in RL. We believe this survey can shed light on future research in DPL.