In problem-solving, we humans tend to come up with different novel solutions to the same problem. However, conventional reinforcement learning algorithms ignore such a feat and only aim at producing a set of monotonous policies that maximize the cumulative reward. The resulting policies usually lack diversity and novelty. In this work, we aim at enabling the learning algorithms with the capacity of solving the task with multiple solutions through a practical novel policy generation workflow that can generate a set of diverse and well-performing policies. Specifically, we begin by introducing a new metric to evaluate the difference between policies. On top of this well-defined novelty metric, we propose to rethink the novelty-seeking problem through the lens of constrained optimization, to address the dilemma between the task performance and the behavioral novelty in existing multi-objective optimization approaches, we then propose a practical novel policy-seeking algorithm, Interior Policy Differentiation (IPD), which is derived from the interior point method commonly known in the constrained optimization literature. Experimental comparisons on benchmark environments show IPD can achieve a substantial improvement over previous novelty-seeking methods in terms of both novelties of generated policies and their performances in the primal task.