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Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport
Miguel Costa, Arthur Vandervoort, Carolin Schmidt +5
Climate change is expected to intensify rainfall and, consequently, pluvial flooding, leading to increased disruptions in urban transportation systems over the coming decades. Desi…
Synthetic Monitoring Environments for Reinforcement Learning
Leonard Pleiss, Carolin Schmidt, Maximilian Schiffer
Reinforcement Learning (RL) lacks benchmarks that enable precise, white-box diagnostics of agent behavior. Current environments often entangle complexity factors and lack ground-tr…
Competitive Multi-Operator Reinforcement Learning for Joint Pricing and Fleet Rebalancing in AMoD Systems
Emil Kragh Toft, Carolin Schmidt, Daniele Gammelli +1
Autonomous Mobility-on-Demand (AMoD) systems promise to revolutionize urban transportation by providing affordable on-demand services to meet growing travel demand. However, realis…
Learning long term climate-resilient transport adaptation pathways under direct and indirect flood impacts using reinforcement learning
Miguel Costa, Arthur Vandervoort, Carolin Schmidt +4
Climate change is expected to intensify rainfall and other hazards, increasing disruptions in urban transportation systems. Designing effective adaptation strategies is challenging…