class_docstr: Implementation for DRL algorithms for portfolio optimization.
  
  Attributes
  ----------
      env: gym environment class
          user-defined class
  Methods
  -------
      get_model()
          setup DRL algorithms
      train_model()
          train DRL algorithms in a train dataset
          and output the trained model
      DRL_prediction()
          make a prediction in a test dataset and get results
code: def get_model(
      self, model_name, model_kwargs=None, policy_kwargs=None
  ):
      if model_name not in MODELS:
          raise NotImplementedError(
              "The model requested was not implemented."
          )
  
      model = MODELS[model_name]
      model_kwargs = {} if model_kwargs is None else model_kwargs
      if policy_kwargs is not None:
          model_kwargs["policy_kwargs"] = policy_kwargs
      return model(self.env, **model_kwargs)
  
imports: from __future__ import annotations
  from .algorithms import PolicyGradient
label_desc: Use image node_id 2 for calling the DRLAgent obj's underlying member method code with example usage: obj.get_model(model_name, model_kwargs, policy_kwargs) and returns: model