Abstract: Systems and methods for routing rechargeable entities are described. A processor can receive input indicating a state of a charging network that includes charging stations, rechargeable entities and objects with assigned destinations. The processor can execute, for each object, a decision making process to model decision making by a reinforcement learning agent. The decision making can include applying a sequence of actions on the charging network to change states of the charging network. The processor can determine a sequence of states of the charging network based on results from application of the sequence of actions. The sequence of states can represent transitions of the rechargeable entities and the objects to complete delivery of the objects to the assigned destinations. The processor can generate routing data to direct the rechargeable entities to navigate among the charging stations and to be coupled with the objects according to the sequence of states.