Atomic Proximal Policy Optimization for Electric Robo-Taxi Dispatch and Charger Allocation

The paper introduces Atomic Proximal Policy Optimization (Atomic-PPO), a deep reinforcement learning algorithm that reduces the action space via atomic action decomposition to efficiently evaluate electric robo-taxi dispatch and charger allocation. Using a discrete-time Markov decision process and real-world NYC for-hire vehicle data, Atomic-PPO achieves near-optimal fleet dispatching policies and outperforms heuristic approaches. Numerical experiments demonstrate how charger placement, charger quantity, charging speed, and fleet size jointly influence system performance, emphasizing ridership-based charger placement and balanced infrastructure investment.

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• reinforcement-learning
• benchmarks