In recent years, deep reinforcement learning (DRL) has achieved remarkable success in many sequential decision-making tasks. However, the current success of deep reinforcement learning heavily relies on massive learning data and computing resources. The poor sample efficiency and strategy generalization ability are the key factors restricting DRL’s further development. Meta-reinforcement learning (Meta-RL) studies to adapt to a wider range of tasks with a smaller sample size. Related researches are expected to alleviate the above limitations and promote the development of reinforcement learning. Taking the scope of research object and application range of current research works, this study comprehensively combs the research progress in the field of meta-reinforcement learning. Firstly, a basic introduction is given to deep reinforcement learning and the background of meta-reinforcement learning. Then, meta-reinforcement learning is formally defined and common scene settings are summarized, and the current research progress of meta-reinforcement learning is also introduced from the perspective of application range of the research results. Finally, the research challenges and potential future development directions are discussed.