The paper investigates how penalty-based regulation can curb surge pricing and enhance service coverage for profit-maximizing Autonomous Mobility-on-Demand operators. It models regulation as an outer control loop, using fixed penalties formulated as a Markov Decision Process and adaptive penalties as a Partially Observable Stochastic Game, with both operator and regulator policies learned via graph-based multi-agent reinforcement learning. Experiments on Manhattan data show that fixed penalties cut excessive pricing from 22% to 1%, while dynamic penalties improve regulator rewards by up to 15.6% when spare fleet capacity is available.
arXiv:2604. 25848v2 Announce Type: replace Abstract: We study city-scale control of electric-vehicle (EV) ride-hailing fleets where dispatch, repositioning, and charging decisions must respect charger and feeder limits under uncertain, spatially correlated demand and travel times.
The paper proposes a multi‑agent reinforcement learning framework that dynamically allocates incentives to drivers, encouraging them to take alternative routes. By doing so, it reduces total travel time by up to 16% and CO₂ emissions by 9% under an unlimited budget, while revealing a trade‑off between the two objectives. The study demonstrates that a carefully weighted multi‑objective function can improve both travel time and emissions beyond baseline levels.
arXiv:2606. 13604v1 Announce Type: new Abstract: Dispatch in three-sided marketplaces provides a natural setting for reinforcement learning from world feedback: decisions are evaluated by delayed operational outcomes such as delivery speed, courier utilization, and merchant congestion.
arXiv:2606. 31347v1 Announce Type: new Abstract: The electrification of transportation through electric vehicles introduces new challenges for power grid management, such as increased peak demand, voltage fluctuations, line overloads, and the integration of variable renewable energy sources.
The paper proposes a scalable traffic modeling approach that uses a single representative large language model (LLM) agent for each homogeneous traveler group, rather than one LLM per traveler. The representative agent maintains a mixed strategy over routes, updates it daily based on positive reinforcement signals, and uses a tunable step size to adjust its strategy. This design improves scalability, stabilizes learning, and produces interpretable dynamics that reproduce realistic behavioral patterns such as the decoy effect and income‑based willingness‑to‑pay differences.
Emergency management assistance programs, such as relief distribution, are essential for delivering necessary supplies to affected communities. However, these programs operate in a decentralized netwo...
Dispatch in three-sided marketplaces provides a natural setting for reinforcement learning from world feedback: decisions are evaluated by delayed operational outcomes such as delivery speed, courier utilization, and merchant congestion. We present a deployed reinforcement learning system at DoorDash that adapts dispatch objective weights in a large-scale food-delivery marketplace using delayed signals.
CoRe-MARL is a cooperative multi-agent reinforcement learning framework designed for decentralized relief distribution networks. It models each local center as an agent in a Dec-POMDP, using a recurrent network to learn redistribution policies that reduce service gaps and improve the worst-served region. Experiments show that recurrent MAPPO outperforms independent PPO and heuristic baselines, maintaining competitive network-wide service while adapting to evolving supply and demand dynamics.
arXiv:2607. 05683v1 Announce Type: new Abstract: Battery charging of Autonomous Mobile Robots (AMRs) in warehouses is a critical operational challenge that heavily impacts both order processing times and throughput.
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.
arXiv:2609.07689v1 Announce Type: new Abstract: In electric delivery fleets, mid-shift charging is non-trivial: each vehicle must decide when, where and how much to charge to finish on time with batt...
arXiv:2607. 23434v1 Announce Type: cross Abstract: Unexpected shocks recur in global operations, requiring decision rules that adapt as market and operating conditions change.