Flatland-RL : Multi-Agent Reinforcement Learning on Trains
This work provides a new, faster experimentation environment for machine learning researchers to tackle the complex vehicle rescheduling problem in railway systems, which is an incremental step towards more efficient train scheduling.
The paper introduces Flatland, a simplified 2D grid environment for multi-agent reinforcement learning to address the vehicle rescheduling problem (VRSP) in train systems. They conducted initial RL and IL experiments, demonstrating ML's potential for VRSP on Flatland, and launched a public benchmark at NeurIPS 2020 to engage researchers.
Efficient automated scheduling of trains remains a major challenge for modern railway systems. The underlying vehicle rescheduling problem (VRSP) has been a major focus of Operations Research (OR) since decades. Traditional approaches use complex simulators to study VRSP, where experimenting with a broad range of novel ideas is time consuming and has a huge computational overhead. In this paper, we introduce a two-dimensional simplified grid environment called "Flatland" that allows for faster experimentation. Flatland does not only reduce the complexity of the full physical simulation, but also provides an easy-to-use interface to test novel approaches for the VRSP, such as Reinforcement Learning (RL) and Imitation Learning (IL). In order to probe the potential of Machine Learning (ML) research on Flatland, we (1) ran a first series of RL and IL experiments and (2) design and executed a public Benchmark at NeurIPS 2020 to engage a large community of researchers to work on this problem. Our own experimental results, on the one hand, demonstrate that ML has potential in solving the VRSP on Flatland. On the other hand, we identify key topics that need further research. Overall, the Flatland environment has proven to be a robust and valuable framework to investigate the VRSP for railway networks. Our experiments provide a good starting point for further research and for the participants of the NeurIPS 2020 Flatland Benchmark. All of these efforts together have the potential to have a substantial impact on shaping the mobility of the future.