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Tiejin Chen

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3 papers
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3

RLC Conference 2025 Conference Paper

Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control

  • Justin Turnau
  • Longchao Da
  • Khoa Vo
  • Ferdous Al Rafi
  • Shreyas Bachiraju
  • Tiejin Chen
  • Hua Wei

Traffic Signal Control (TSC) is essential for managing urban traffic flow and reducing congestion. Reinforcement Learning (RL) offers an adaptive method for TSC by responding to dynamic traffic patterns, with Multi-agent RL (MARL) gaining traction as intersections naturally function as coordinated agents. However, due to shifts in environmental dynamics, implementing MARL-based TSC policies in the real world often leads to a significant performance drop, known as the sim-to-real gap. Grounded Action Transformation (GAT) has successfully mitigated this gap in single-agent RL for TSC, but real-world traffic networks, which involve numerous interacting intersections, are better suited to a MARL framework. In this work, we introduce JL-GAT, an application of GAT to MARL-based TSC that balances scalability with enhanced grounding capability by incorporating information from neighboring agents. JL-GAT adopts a decentralized approach to GAT, allowing for the scalability often required in real-world traffic networks while still capturing key interactions between agents. Comprehensive experiments on various road networks and ablation studies demonstrate the effectiveness of JL-GAT.

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RLJ Journal 2025 Journal Article

Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control

  • Justin Turnau
  • Longchao Da
  • Khoa Vo
  • Ferdous Al Rafi
  • Shreyas Bachiraju
  • Tiejin Chen
  • Hua Wei

Traffic Signal Control (TSC) is essential for managing urban traffic flow and reducing congestion. Reinforcement Learning (RL) offers an adaptive method for TSC by responding to dynamic traffic patterns, with Multi-agent RL (MARL) gaining traction as intersections naturally function as coordinated agents. However, due to shifts in environmental dynamics, implementing MARL-based TSC policies in the real world often leads to a significant performance drop, known as the sim-to-real gap. Grounded Action Transformation (GAT) has successfully mitigated this gap in single-agent RL for TSC, but real-world traffic networks, which involve numerous interacting intersections, are better suited to a MARL framework. In this work, we introduce JL-GAT, an application of GAT to MARL-based TSC that balances scalability with enhanced grounding capability by incorporating information from neighboring agents. JL-GAT adopts a decentralized approach to GAT, allowing for the scalability often required in real-world traffic networks while still capturing key interactions between agents. Comprehensive experiments on various road networks and ablation studies demonstrate the effectiveness of JL-GAT.

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AAAI Conference 2024 Conference Paper

Uncertainty Regularized Evidential Regression

  • Kai Ye
  • Tiejin Chen
  • Hua Wei
  • Liang Zhan

The Evidential Regression Network (ERN) represents a novel approach that integrates deep learning with Dempster-Shafer's theory to predict a target and quantify the associated uncertainty. Guided by the underlying theory, specific activation functions must be employed to enforce non-negative values, which is a constraint that compromises model performance by limiting its ability to learn from all samples. This paper provides a theoretical analysis of this limitation and introduces an improvement to overcome it. Initially, we define the region where the models can't effectively learn from the samples. Following this, we thoroughly analyze the ERN and investigate this constraint. Leveraging the insights from our analysis, we address the limitation by introducing a novel regularization term that empowers the ERN to learn from the whole training set. Our extensive experiments substantiate our theoretical findings and demonstrate the effectiveness of the proposed solution.

PDF Details DOI
v2026.09.13