Оптимізація руху транспорту в простій мережі за допомогою глибокого навчання з підкріпленням

Traffic flow optimization in urban environments remains one of the key challenges of modern research, even despite the significant volume of scientific works devoted to this topic. Despite the achievements, this problem still does not have a universal solution that would work effectively in real-world scenarios. One of the main difficulties is the processing of a large array of input data, in particular, traffic data, which constantly comes from sensors installed throughout the urban road network. Traditionally, due to the scale of the task, researchers have focused on the development of systems with localized agents. Such agents usually manage traffic at individual intersections, while their coordination is carried out within the framework of multi-stream agent systems. However, modern approaches take into account the volume and complexity of input data through the use of deep learning methods. In particular, the use of the deep deterministic policy gradient (DDPG) algorithm is proposed, on the basis of which large input data can be processed. As part of the experimental study, a simple intersection model was tested to verify the effectiveness of the approach. The DDPG algorithm performed better in the simple model compared to Q-learning. DDPG provided a reward in the range of 4-4.3 points, while the reward of Q-learning was in the range of 2-4 points. To evaluate the performance of the DDPG approach compared to Q-learning and random timings, the main criterion is the average reward per episode. DDPG and Q-learning achieve similar reward levels, but DDPG shows stable convergence (0.04-0.21 points), while Q-learning remains unstable (0.04-0.43 points). The study of intra-episode performance shows that DDPG achieves improvements mainly closer to the end of the episode. Overall, this algorithm has proven successful for this scenario, and the results obtained can serve as a basis for further improvements and applications in more complex traffic scenarios.