TRAFFIC LIGHT CONTROL SYSTEM SIMULATOR FOR REINFORCEMENT LEARNING HARDWARE ACCELERATOR - Haritsa Maulana, Daffa
Traffic light regulation is very crucial in urban areas. The more efficient the regulatory system, the more benefits will be obtained. Reinforcement Learning is an algorithm that will be tested to become the basis for the traffic light control system. Testing is done by modeling the intersection scheme with certain parameters using the python language and software. The simulator is getting action from hardware accelerator and give the action to the environment. The simulator also inteperate the congestion condition and convert it to state. Comparison between Reinforcement Learning method and fixed-time method was also conducted. From the results of this final project, it is found that using the reinforcement learning method can reduce congestion by up to 15% when compared to the fixed-time method.