Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866910452068384768 |
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| author | Hoppe, Heiko Enders, Tobias Cappart, Quentin Schiffer, Maximilian |
| author_facet | Hoppe, Heiko Enders, Tobias Cappart, Quentin Schiffer, Maximilian |
| contents | We study vehicle dispatching in autonomous mobility on demand (AMoD) systems, where a central operator assigns vehicles to customer requests or rejects these with the aim of maximizing its total profit. Recent approaches use multi-agent deep reinforcement learning (MADRL) to realize scalable yet performant algorithms, but train agents based on local rewards, which distorts the reward signal with respect to the system-wide profit, leading to lower performance. We therefore propose a novel global-rewards-based MADRL algorithm for vehicle dispatching in AMoD systems, which resolves so far existing goal conflicts between the trained agents and the operator by assigning rewards to agents leveraging a counterfactual baseline. Our algorithm shows statistically significant improvements across various settings on real-world data compared to state-of-the-art MADRL algorithms with local rewards. We further provide a structural analysis which shows that the utilization of global rewards can improve implicit vehicle balancing and demand forecasting abilities. Our code is available at https://github.com/tumBAIS/GR-MADRL-AMoD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_08884 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems Hoppe, Heiko Enders, Tobias Cappart, Quentin Schiffer, Maximilian Machine Learning Multiagent Systems Systems and Control We study vehicle dispatching in autonomous mobility on demand (AMoD) systems, where a central operator assigns vehicles to customer requests or rejects these with the aim of maximizing its total profit. Recent approaches use multi-agent deep reinforcement learning (MADRL) to realize scalable yet performant algorithms, but train agents based on local rewards, which distorts the reward signal with respect to the system-wide profit, leading to lower performance. We therefore propose a novel global-rewards-based MADRL algorithm for vehicle dispatching in AMoD systems, which resolves so far existing goal conflicts between the trained agents and the operator by assigning rewards to agents leveraging a counterfactual baseline. Our algorithm shows statistically significant improvements across various settings on real-world data compared to state-of-the-art MADRL algorithms with local rewards. We further provide a structural analysis which shows that the utilization of global rewards can improve implicit vehicle balancing and demand forecasting abilities. Our code is available at https://github.com/tumBAIS/GR-MADRL-AMoD. |
| title | Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems |
| topic | Machine Learning Multiagent Systems Systems and Control |
| url | https://arxiv.org/abs/2312.08884 |