Scheduling in multiagent systems using reinforcement learning

The paper is devoted to scheduling in multiagent systems in the framework of the Flatland 3 competition. The main aim of this competition is to develop an algorithm for the effective control of dense traffic in complex railroad networks according to a given schedule. The proposed solution is based on reinforcement learning. To adapt this method to the particular scheduling problem, a novel approach based on structuring the reward function that stimulates an agent to adhere to its schedule was developed. The architecture of the proposed model is based on a multiagent version of centralized critic with proximal policy optimization (PPO) learning. In addition, a curriculum learning strategy was developed and implemented. This allowed the agent to cope with each level of complexity on time and train the model in more difficult conditions. The proposed solution won first place in the Flatland 3 competition in the reinforcement learning track.