  Electric vehicles (EVs) play critical roles in autonomous mobility-on-demand
(AMoD) systems, but their unique charging patterns increase the model
uncertainties in AMoD systems (e.g. state transition probability). Since there
usually exists a mismatch between the training and test/true environments,
incorporating model uncertainty into system design is of critical importance in
real-world applications. However, model uncertainties have not been considered
explicitly in EV AMoD system rebalancing by existing literature yet, and the
coexistence of model uncertainties and constraints that the decision should
satisfy makes the problem even more challenging. In this work, we design a
robust and constrained multi-agent reinforcement learning (MARL) framework with
state transition kernel uncertainty for EV AMoD systems. We then propose a
robust and constrained MARL algorithm (ROCOMA) with robust natural policy
gradients (RNPG) that trains a robust EV rebalancing policy to balance the
supply-demand ratio and the charging utilization rate across the city under
model uncertainty. Experiments show that the ROCOMA can learn an effective and
robust rebalancing policy. It outperforms non-robust MARL methods in the
presence of model uncertainties. It increases the system fairness by 19.6% and
decreases the rebalancing costs by 75.8%.
