Low-Cost Traffic Control using Reinforcement Learning

Solving the traffic congestion problem has many benefits financially and environmentally. The application of Artificial Intelligence to solving the traffic congestion problem has been going on for a while. However, most of the current research in this area depends on knowing lots of information about all vehicles in the network. While it produces promising results, applying these techniques in the current world is not easy. In this paper, we apply reinforcement learning to the field of traffic control under the assumption that only minimal information is available. Our approach produces results that are better than currently deployed fixed-time traffic lights without having heavy requirements. In our first test configuration, our agent's waiting time is 82.3% of the best fixed-time traffic lights' waiting time and the average CO2 emissions produced by our agent is 97.5% of the emissions produced by the best fixed-time traffic lights. This shows the potential of applying reinforcement learning to the traffic control problem with limited state.