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

• URL: http://arxiv.org/abs/2307.12388v3
• Date: Mon, 30 Oct 2023 01:16:22 GMT
• Title: Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control
• Abstract summary: We propose a simulation-to-real-world (sim-to-real) transfer approach called UGAT. 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.
• Score: 3.6216188129806643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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