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Adding Pre-trained/Rule-based models

You can add your own pre-trained/rule-based models to the toolkit by following several steps:

  • Develop models. You can either design a rule-based model save a neural network model. For each game, you need to develop models for all the players at the same time. You need to wrap each model as a class and make sure that step and eval_step can work correctly.
  • Wrap models. You need to inherit the Model class in rlcard/models.model.py. Then put all the models for the players into a list. Rewrite get_agent function and return this list.
  • Register the model. Register the model in rlcard/models/__init__.py.
  • Load the model in environment. To load the model, modify load_pretrained_models in the corresponding game environment in rlcard/envs. Use the resgistered name to load the model.