Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems Article Swipe
Heiko Hoppe
,
Tobias Enders
,
Quentin Cappart
,
Maximilian Schiffer
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.08884
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2312.08884
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.
Related Topics
Concepts
Reinforcement learning
Computer science
Counterfactual thinking
Scalability
Profit (economics)
Distributed computing
Operator (biology)
Artificial intelligence
Operations research
Engineering
Economics
Repressor
Gene
Microeconomics
Biochemistry
Philosophy
Transcription factor
Chemistry
Database
Epistemology
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2312.08884
- https://arxiv.org/pdf/2312.08884
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389820725
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389820725Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2312.08884Digital Object Identifier
- Title
-
Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand SystemsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-12-14Full publication date if available
- Authors
-
Heiko Hoppe, Tobias Enders, Quentin Cappart, Maximilian SchifferList of authors in order
- Landing page
-
https://arxiv.org/abs/2312.08884Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2312.08884Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2312.08884Direct OA link when available
- Concepts
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Reinforcement learning, Computer science, Counterfactual thinking, Scalability, Profit (economics), Distributed computing, Operator (biology), Artificial intelligence, Operations research, Engineering, Economics, Repressor, Gene, Microeconomics, Biochemistry, Philosophy, Transcription factor, Chemistry, Database, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.assigning | 96 |
| abstract_inverted_index.available | 150 |
| abstract_inverted_index.balancing | 142 |
| abstract_inverted_index.baseline. | 103 |
| abstract_inverted_index.conflicts | 87 |
| abstract_inverted_index.therefore | 68 |
| abstract_inverted_index.abilities. | 146 |
| abstract_inverted_index.algorithms | 120 |
| abstract_inverted_index.approaches | 32 |
| abstract_inverted_index.autonomous | 5 |
| abstract_inverted_index.leveraging | 100 |
| abstract_inverted_index.maximizing | 27 |
| abstract_inverted_index.performant | 43 |
| abstract_inverted_index.real-world | 114 |
| abstract_inverted_index.structural | 128 |
| abstract_inverted_index.algorithms, | 44 |
| abstract_inverted_index.dispatching | 3, 77 |
| abstract_inverted_index.forecasting | 145 |
| abstract_inverted_index.multi-agent | 34 |
| abstract_inverted_index.significant | 108 |
| abstract_inverted_index.system-wide | 61 |
| abstract_inverted_index.utilization | 134 |
| abstract_inverted_index.improvements | 109 |
| abstract_inverted_index.performance. | 66 |
| abstract_inverted_index.reinforcement | 36 |
| abstract_inverted_index.statistically | 107 |
| abstract_inverted_index.counterfactual | 102 |
| abstract_inverted_index.state-of-the-art | 118 |
| abstract_inverted_index.global-rewards-based | 72 |
| abstract_inverted_index.https://github.com/tumBAIS/GR-MADRL-AMoD. | 152 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |