Computer Science > Machine Learning
[Submitted on 23 Jul 2023 (v1), last revised 30 Oct 2023 (this version, v3)]
Title:Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control
View PDFAbstract: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.
Submission history
From: Longchao Da [view email][v1] Sun, 23 Jul 2023 17:35:49 UTC (11,918 KB)
[v2] Tue, 24 Oct 2023 03:40:56 UTC (11,893 KB)
[v3] Mon, 30 Oct 2023 01:16:22 UTC (11,893 KB)
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