Method and apparatus for controlling traffic light

A method for controlling a traffic light, applied to a traffic light control end communicating with an unmanned vehicle navigation end, the method comprising:

• generating a reinforced traffic light state parameter according to vehicle state representation information of an unmanned vehicle currently contained in a preset area of a target traffic light and a current traffic light state parameter of the target traffic light;
• and
• generating, according to the reinforced traffic light state parameter, a traffic light control action matching the reinforced traffic light state parameter, wherein the generating the traffic light control action matching the reinforced traffic light state parameter according to the reinforced traffic light state parameter comprises:
• inputting the reinforced traffic light state parameter into a first reinforcement learning model, to obtain the traffic light control action matching the reinforced traffic light state parameter;
• wherein the reinforced traffic light state parameter is used to cause the unmanned vehicle navigation end to:
• generate a reinforced vehicle state parameter according to the reinforced traffic light state parameter and a current vehicle state parameter of the unmanned vehicle, and generate an unmanned vehicle navigation action matching the reinforced vehicle state parameter according to the reinforced vehicle state parameter,
• wherein the method further comprises training the first reinforcement learning model, and the training comprises:
• generating a sample reinforced traffic light state parameter according to sample vehicle state representation information of a sample unmanned vehicle currently contained in a sample preset area of a sample target traffic light and a sample current traffic light state parameter of the sample target traffic light;
• inputting the sample reinforced traffic light state parameter into the first reinforcement learning model, to obtain a sample traffic light control action matching the sample reinforced traffic light state parameter;
• performing the sample traffic light control action, to obtain a new traffic light state parameter and a first reward parameter;
• and
• determining a first loss value, based on the first reward parameter, the new traffic light state parameter, and the sample reinforced traffic light state parameter;
• and
• training the first reinforcement learning model according to the first loss value.