Abstract: Smart particles in the present study refer to the particles in fluid that can actively adjust their motions based on the changing environment, and they are usually used to describe micro-swimmers such as microorganisms, plankton, or micro-robots. Due to the complex dynamics of particles and the flow environment, exploring the swimming strategy of smart particles is challenging but of great practical significance. Recently, reinforcement learning has been adopted for exploring the swimming strategies of smart particles, and certain progress was made. Here, we discuss the application of reinforcement learning in the study of smart particles, and introduce the recent progresses in the swimming strategy of plankton, including the swimming particle model for marine plankton, and the framework of reinforcement learning. The vertical migration is vital to the survival and reproduction of plankton. Biological study suggested that some plankton can perceive information from local fluid environment, but whether this information can be used for accelerating vertical migration still remains unknown. In this context, researchers investigated the influence of gravitational settling and particle shape on the vertical swimming strategy of plankton. Swimmers with slender shape can navigate upward more efficiently, and gravitational settling results in significant changes in smart swimming strategies. Furthermore, successive studies were carried out to investigate the effect of local fluid signals, and to discuss the possibility of navigation in the global frame of reference with only local signals. When swimmers only access to local signals, they cannot learn any effective upward swimming strategy unless the rotational symmetry of the dynamics is broken. Moreover, it was also found that reinforcement learning can make use of the underlying physical mechanism of local signals, and obtain efficient swimming strategies for vertical migration in two-dimensional time-independent flows and three-dimensional turbulent flow. Because the mechanism behind these strategies is essential and robust, these strategies is expected to be effective in more complex and realistic flow environments.