Existing traffic signal controllers are often inadequate in handling traffic congestion and reinforcement learning (RL) has been adopted to solve this problem. However, RL usually works in a stationary environment, while traffic environments are nonstationary. Researchers have now developed a meta-RL model that adjusts its goal based on the traffic environment, maximizing vehicle throughput during peak hours and minimizing delays during relaxed traffic flow. The algorithm has a wide-area coverage and outperforms existing alternative algorithms.
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