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SafeLight: A Reinforcement Learning Method toward Collision-Free Traffic Signal Control

AAAI 2023 · Reinforcement learning › Visual and representation RL · approach: RL for combinatorial routing · 49 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add safety-rule action filtering3DQN · Synthetic dataset, cyclic actionscollisions: 93.94% fewer collisionsHelped
Add safety-rule action filtering3DQN · Real-world datasetcollisions: 86% fewer collisionsHelped
Add safety-based reward shaping3DQN · Synthetic settingcollisions: 99.32% improvement in safetyHelped
Add a safety module to the RL methodBackbone RL (3DQN) · Synthetic and real-world settingscollisions: over 99 percent reduction in collisionsHelped
Add a safety module to the RL methodBackbone RL (3DQN) · Compared with Fixed-time controlaverage waiting time: about 30 percent lower average waiting timeHelped
Add an additional safety feature to the state representation (ablation)SafeLight-R or SafeLight-Loss · Three evaluation settingsoverall performanceNo clear effect
Use a synthetic weighted-Q objective instead of the backbone objective3DQN · Compared with Backbone RL (3DQN)average waiting timeHurt
Use synthetic reward shaping3DQN · Cyclic versus acyclic action settingscollisionsMixed
Use acyclic rather than cyclic actionsTraffic signal controllers · Different action-space settingscollisionsHurt
Add a safety penalty to the rewardIPPO · Synthetic intersectioncollisions: 24.5 -> 0.0Helped
Add a safety objective to the lossIPPO · Synthetic intersectionaverage delay: 10.66 -> 13.07Hurt

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