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arXiv · 2307.12388

Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control

Longchao Da·Hao Mei·Romir Sharma·Hua Wei
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Abstract

Traffic signal control (TSC) is a complex and important task that affects the daily lives of millions of people. Reinforcement Learning (RL) has shown promising results in optimizing traffic signal control, but current RL-based TSC methods are mainly trained in simulation and suffer from the performance gap between simulation and the real world. In this paper, we propose a simulation-to-real-world (sim-to-real) transfer approach called UGAT, which transfers a learned policy trained from a simulated environment to a real-world environment by dynamically transforming actions in the simulation with uncertainty to mitigate the domain gap of transition dynamics. We evaluate our method on a simulated traffic environment and show that it significantly improves the performance of the transferred RL policy in the real world.

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Longchao Da, Hao Mei, Romir Sharma, Hua Wei. 2023-07-23. Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control. https://arxiv.org/abs/2307.12388

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