Deep Reinforcement Learning for Multidriver Vehicle Dispatching and Repositioning Problem Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ridesharing platform marketplace. Handcrafting heuristic solutions that account for the dynamics in these resource allocation problems is difficult, and may be better handled by an endtoend machine learning method. Previous works have explored machine learning methods to the problem from a highlevel perspective, where the learning method is responsible for either repositioning the drivers or dispatching orders, and as a further simplification, the drivers are considered independent agents maximizing their own reward functions. In this paper we present a deep reinforcement learning approach for tackling the full fleet management and dispatching problems. In addition to treating the drivers as individual agents, we consider the problem from a systemcentric perspective, where a central fleet management agent is responsible for decisionmaking for all drivers.

MultiAgent Deep Reinforcement Learning for Cooperative Driving in Crowded Traffic Scenarios For autonomous vehicles, lane changes on crowded roads are difficult to be performed without interactions and cooperation between vehicles. This paper proposes a novel method to learn interaction and cooperate between the multiple vehicles to solve the complex traffic problem through MultiAgent Reinforcement Learning (MARL). The proposed network is designed based on the interaction network to learn optimal control strategies considering interaction between vehicles. By applying the proposed algorithm, the network can control and train the agents regardless of the number of agents. It is a practical advantage because the number of the vehicles is constantly changed in the real environment. The proposed method is evaluated in the connected car environment where all vehicles can exchange information with each other.