Estimating individualized treatment rules is a central task of personalized or precision medicine. In this presentation, we review several new developments in outcome weighted learning for identifying individualized treatment rules for two challenging settings: dose finding and treatment selection under right censoring. In the former, we develop an approach which involves the use of two different kernels; and in the later, we use random forests to address censoring before applying outcome weighted learning. The performance of the methods are evaluated both theoretically and numerically and demonstrated to have many advantages. We also discuss several open research questions and the future potential of policy learning approach, such as outcome weighted learning, in precision medicine.