<p>It is a key issue for determining the optimal treatment regime or a sequence of treatment regimes based on individual characteristics in precision medical research. Most existing studies on estimating optimal treatment regimes focus on maximizing the average return on the potential outcomes of interest. However, this approach fails to capture the potential heterogeneity of observations and can not provide a complete characterization of the data. Expectiles, derived from an asymmetric quadratic loss function, encompass the mean and serve as a valuable tool for describing the distribution of outcomes. Motivated by these advantages of expectiles, we propose novel estimators for both static and dynamic expectile-optimal treatment regimes. Due to the differentiability of the loss function, it can bring computational advantages and facilitates theoretical analysis. The asymptotic properties of the proposed estimators are derived using empirical process theory. Their good finite sample performances are demonstrated through simulations and a real data from the HIV patients.</p>