Title: Reinforcement Logic Rule Learning for Temporal Point Processes . Abstract: We aim to learn a set of temporal logic rules to explain the occurrence of temporal events. Leveraging the temporal point process modeling and learning framework, the rule content and rule weights are jointly learned by maximizing the likelihood of the observed noisy event sequences. The proposed algorithm alternates between a master problem, where the rule weights are updated, and a subproblem, where a new rule is searched and included. The formulated master problem is convex and relatively easy to solve, whereas the subproblem requires searching the huge combinatorial rule predicate and relationship space. To tackle this challenge, we propose a neural search policy to learn to generate the new rule content as a sequence of actions. The policy parameters will be trained end-to-end using the reinforcement learning framework, where the reward signals can be efficiently queried by evaluating the subproblem objective. The trained policy can be used to generate new rules, and moreover, the well-trained policies can be directly transferred to other tasks to speed up the rule searching procedure in the new task. We evaluate our methods on both synthetic and real-world datasets, obtaining promising results.