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 train_model(model, episodes=100):
      model.train(episodes)
  
imports: from __future__ import annotations
  from .algorithms import PolicyGradient
label_desc: Use image node_id 3 for calling the DRLAgent obj's underlying member method code with example usage: obj.train_model(model, episodes) without return types