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Run Model Pipline

Our library has a uniform structure that empowers users to start their experiments with just one click. Users can start an experiment by setting arguments in the run.py file and start with their customized settings. The following part is the arguments provided to customize.

python run.py

Supporing parameters:

• thread_num: number of threads for cityflow simulation

• ngpu: how many gpu resources used in this experiment

• task: task type to run

• agent: agent type of agents in RL environment

• world: simulator type

• dataset: type of dataset in training process

• path: path to configuration file

• prefix: the number of predix in this running process

• seed: seed for pytorch backend