An Efficient Deep Reinforcement Learning Model for Urban Traffic Control
This work addresses urban traffic management for intelligent transportation systems, but it appears incremental as it builds on existing reinforcement learning methods with specific improvements.
The paper tackles the problem of urban traffic control at multiple intersections by proposing a deep reinforcement learning algorithm that relaxes fixed traffic demand assumptions and reduces manual tuning, showing it outperforms traditional rule-based approaches in simulations.
Urban Traffic Control (UTC) plays an essential role in Intelligent Transportation System (ITS) but remains difficult. Since model-based UTC methods may not accurately describe the complex nature of traffic dynamics in all situations, model-free data-driven UTC methods, especially reinforcement learning (RL) based UTC methods, received increasing interests in the last decade. However, existing DL approaches did not propose an efficient algorithm to solve the complicated multiple intersections control problems whose state-action spaces are vast. To solve this problem, we propose a Deep Reinforcement Learning (DRL) algorithm that combines several tricks to master an appropriate control strategy within an acceptable time. This new algorithm relaxes the fixed traffic demand pattern assumption and reduces human invention in parameter tuning. Simulation experiments have shown that our method outperforms traditional rule-based approaches and has the potential to handle more complex traffic problems in the real world.