November 10, 2025SN Computer ScienceOpen Access

A Survey on Multi-agent Reinforcement Learning for Adaptive Transportation Solutions

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Authors

JLJintao Liang XDXinqi Du YLYilin Liu

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Implication

This survey reviews adaptive solutions for transportation systems using multi-agent reinforcement learning, indicating challenges in scalability and policy transferability.

Key Points

Adaptive transport solutions improve dynamic decision-making across varied environments, boosting efficiency.
Key challenges include policy transferability and scalability in RL systems, impacting real-world applications.
Analysis of transportation systems focuses on traffic signal control and air traffic management, emphasizing the need for coordination.
Future directions include enhancing adaptability via cross-scenario multi-agent reinforcement learning techniques.

Cite This Study

Liang et al. (2025) studied this question.

synapsesocial.com/papers/69253a29c0ce034ddc35747f https://doi.org/10.1007/s42979-025-04475-3

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Implication

This survey reviews adaptive solutions for transportation systems using multi-agent reinforcement learning, indicating challenges in scalability and policy transferability.

A Survey on Multi-agent Reinforcement Learning for Adaptive Transportation Solutions

Discussion

Member takes

Implication

Key Points

Cite This Study

A Survey on Multi-agent Reinforcement Learning for Adaptive Transportation Solutions

Discussion

Member takes

Implication

Key Points

Cite This Study

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Also Consider