Reinforcement learning (RL) is a research hotpot in the machine learning area, which is considering a process of agent-environment interaction, sequential decision making, and total reward maximization. Reinforcement learning is worthy of in-depth research and a wide range of applications in the real world, and represents a vital step toward the Artificial General Intelligence (AGI). In this survey, we review the research progress and development in the algorithms and applications for reinforcement learning. We start with a brief review of the principle of reinforcement learning, including Markov decision process, value function, and exploration v.s. exploitation. Next, we discuss the traditional RL algorithms, including value-based algorithms, policy-based algorithms, and Actor-Critic algorithms, and further discuss the frontiers of RL algorithms, including multi-agent reinforcement learning and meta reinforcement learning. Then, we sketch some successful RL applications in the fields of games, robotics, urban traffic, and business. Finally, we summarize briefly and prospect the development trends of reinforcement learning.