Scalable Deep Reinforcement Learning for Ride-Hailing
This addresses the problem of efficiently managing large-scale ride-hailing fleets for companies like Didi Chuxing, Lyft, and Uber, though it appears incremental as it builds on existing MDP and RL frameworks.
The paper tackled the scalability challenge in ride-hailing services by proposing a decomposition method for Markov decision process actions, enabling the use of deep reinforcement learning algorithms, and demonstrated its benefit with a numerical experiment using real data from Didi Chuxing.
Ride-hailing services, such as Didi Chuxing, Lyft, and Uber, arrange thousands of cars to meet ride requests throughout the day. We consider a Markov decision process (MDP) model of a ride-hailing service system, framing it as a reinforcement learning (RL) problem. The simultaneous control of many agents (cars) presents a challenge for the MDP optimization because the action space grows exponentially with the number of cars. We propose a special decomposition for the MDP actions by sequentially assigning tasks to the drivers. The new actions structure resolves the scalability problem and enables the use of deep RL algorithms for control policy optimization. We demonstrate the benefit of our proposed decomposition with a numerical experiment based on real data from Didi Chuxing.