Title: ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning. Abstract: We propose ScheduleNet, an RL-based decentralized constructive scheduler for coordinating multi-agent to finish tasks with minimum completion time. We formulate multi-agent scheduling problems (mSPs) as an event-based Markov decision process (MDP) with an episodic reward (e.g., makespan) and derive a decentralized decision-making policy using reinforcement learning. The decision making procedure of ScheduleNet includes: (1) representing the state of a scheduling problem with the agent-task graph, (2) extracting node embeddings for agent and tasks nodes by employing the type-aware graph attention (TGA), and (3) computing the assignment probability with the computed node embeddings. We validate the effectiveness of ScheduleNet on two types of mSPs: multiple traveling salesmen problem (mTSP) and job-shop scheduling problem (JSP). We empirically show that ScheduleNet can outperform other heuristic approaches and existing deep RL approaches, particularly validating its exceptional effectiveness in solving large and practical problems. Furthermore, we have demonstrated that ScheduleNet can effectively solve online vehicle routing problems where the new target customer appears dynamically during the course of scheduling.