10-city European rail: Proximal Policy Optimization vs Deep Q-Network, 15-min transfers

PPO beats DQN for 10-city European rail itineraries with over 10^6 transfer combinations and 15-minute constraints. Learn when each algorithm wins. Independent technical note mirroring the canonical version: https://smarttripai.com/blog/10-city-european-rail-proximal-policy-optimization-vs-deep-q-network-15-min-transfers.php Sources consulted: https://www.bluusun.com/

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-27
DOI
https://doi.org/10.5281/zenodo.23721719
Primary Topic
Reinforcement Learning in Robotics
Type
preprint
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10-city European rail: Proximal Policy Optimization vs Deep Q-Network, 15-min transfers

Anna Rose
Zenodo (CERN European Organization for Nuclear Research)
Reinforcement Learning in Robotics
preprint

10-city European rail: Proximal Policy Optimization vs Deep Q-Network, 15-min transfers

Anna Rose
preprint en

Abstract

PPO beats DQN for 10-city European rail itineraries with over 10^6 transfer combinations and 15-minute constraints. Learn when each algorithm wins. Independent technical note mirroring the canonical version: https://smarttripai.com/blog/10-city-european-rail-proximal-policy-optimization-vs-deep-q-network-15-min-transfers.php Sources consulted: https://www.bluusun.com/

Zenodo (CERN European Organization for Nuclear Research)
Reinforcement Learning in Robotics
DOIOpen Access PDFOpenAlex
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10-city European rail: Proximal Policy Optimization vs Deep Q-Network, 15-min transfers — Anna Rose · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS