Decision-making is a crucial intelligence shared by animals and humans and it helps them to act in an intricate environment to seek rewards and avoid punishments. Psychologists first became interested in this ability and studied the conditional behavioral problems associated with it, then these studies have further led to the need for unified quantitative explanation models, among which Reinforcement learning is still the most convincing and data-backed model today. The model itself, in turn, facilitates research in neuroscience. In this paper, the researcher first introduces the original framework of reinforcement learning and the potential neural correlates to it. Then the paper reviews new developments in reinforcement learning algorithms that address the limitations of the original model as well as variants further inspired by neuroscience. Finally, the study highlights some new directions for future research. This study focuses on the evolution of reinforcement learning algorithms inspired by neuroscience, shows the relationship of mutual promotion and common development between reinforcement Learning and neuroscience, and clarifies some concerns for future exploration.