Research map / Reinforcement learning / Visual and representation RL
MetaLight: Value-Based Meta-Reinforcement Learning for Traffic Signal Control
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| value-based meta-RL initialization (MetaLight) instead of pretrained model | FRAP++ · Hangzhou Task-1 homogeneous | travel time: average improvement 5.52% | Mixed |
| value-based meta-RL initialization (MetaLight) instead of pretrained model | FRAP++ · Hangzhou Task-2 heterogeneous | travel time: 22.57% average | Helped |
| value-based meta-RL initialization (MetaLight) | FRAP++ · Task-3 cross-city | travel time: 21.09% homogeneous, 9.59% heterogeneous | Helped |
| policy-style episodic MAML meta-learning instead of random init | FRAP++ · Hangzhou Task-2 | travel time | No clear effect |
Closest papers by meaning
- π-Light: Programmatic Interpretable Reinforcement Learning for Resource-Limited Traffic Signal Control (AAAI 2024 · similarity 0.90)
- RobustLight: Improving Robustness via Diffusion Reinforcement Learning for Traffic Signal Control (ICML 2025 · similarity 0.88)
- VLMLight: Safety-Critical Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning Architecture (NeurIPS 2025 · similarity 0.87)
- DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data (NeurIPS 2024 · similarity 0.87)
- Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning (AAAI 2024 · similarity 0.86)
- GARLIC: GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching (AAAI 2025 · similarity 0.85)
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