Generation of Traffic Flows in Multi-Agent Traffic Simulation with Agent Behavior Model based on Deep Reinforcement Learning Article Swipe
Junjie Zhong
,
Hiromitsu Hattori
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2101.03230
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2101.03230
In multi-agent based traffic simulation, agents are always supposed to move following existing instructions, and mechanically and unnaturally imitate human behavior. The human drivers perform acceleration or deceleration irregularly all the time, which seems unnecessary in some conditions. For letting agents in traffic simulation behave more like humans and recognize other agents' behavior in complex conditions, we propose a unified mechanism for agents learn to decide various accelerations by using deep reinforcement learning based on a combination of regenerated visual images revealing some notable features, and numerical vectors containing some important data such as instantaneous speed. By handling batches of sequential data, agents are enabled to recognize surrounding agents' behavior and decide their own acceleration. In addition, we can generate a traffic flow behaving diversely to simulate the real traffic flow by using an architecture of fully decentralized training and fully centralized execution without violating Markov assumptions.
Related Topics
Concepts
Computer science
Reinforcement learning
Acceleration
Traffic flow (computer networking)
Traffic simulation
Artificial intelligence
Distributed computing
Markov chain
Simulation
Reinforcement
Real-time computing
Machine learning
Computer security
Engineering
Microsimulation
Transport engineering
Physics
Classical mechanics
Structural engineering
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2101.03230
- https://arxiv.org/pdf/2101.03230
- OA Status
- green
- References
- 15
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3118440862
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3118440862Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2101.03230Digital Object Identifier
- Title
-
Generation of Traffic Flows in Multi-Agent Traffic Simulation with Agent Behavior Model based on Deep Reinforcement LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-12-26Full publication date if available
- Authors
-
Junjie Zhong, Hiromitsu HattoriList of authors in order
- Landing page
-
https://arxiv.org/abs/2101.03230Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2101.03230Direct 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/2101.03230Direct OA link when available
- Concepts
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Computer science, Reinforcement learning, Acceleration, Traffic flow (computer networking), Traffic simulation, Artificial intelligence, Distributed computing, Markov chain, Simulation, Reinforcement, Real-time computing, Machine learning, Computer security, Engineering, Microsimulation, Transport engineering, Physics, Classical mechanics, Structural engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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15Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.following | 11 |
| abstract_inverted_index.important | 90 |
| abstract_inverted_index.mechanism | 60 |
| abstract_inverted_index.numerical | 86 |
| abstract_inverted_index.recognize | 49, 106 |
| abstract_inverted_index.revealing | 81 |
| abstract_inverted_index.violating | 144 |
| abstract_inverted_index.containing | 88 |
| abstract_inverted_index.sequential | 100 |
| abstract_inverted_index.simulation | 43 |
| abstract_inverted_index.centralized | 141 |
| abstract_inverted_index.combination | 76 |
| abstract_inverted_index.conditions, | 55 |
| abstract_inverted_index.conditions. | 37 |
| abstract_inverted_index.irregularly | 28 |
| abstract_inverted_index.multi-agent | 1 |
| abstract_inverted_index.regenerated | 78 |
| abstract_inverted_index.simulation, | 4 |
| abstract_inverted_index.surrounding | 107 |
| abstract_inverted_index.unnaturally | 17 |
| abstract_inverted_index.unnecessary | 34 |
| abstract_inverted_index.acceleration | 25 |
| abstract_inverted_index.architecture | 134 |
| abstract_inverted_index.assumptions. | 146 |
| abstract_inverted_index.deceleration | 27 |
| abstract_inverted_index.mechanically | 15 |
| abstract_inverted_index.acceleration. | 114 |
| abstract_inverted_index.accelerations | 67 |
| abstract_inverted_index.decentralized | 137 |
| abstract_inverted_index.instantaneous | 94 |
| abstract_inverted_index.instructions, | 13 |
| abstract_inverted_index.reinforcement | 71 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.6800000071525574 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |