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 __init__(self, env):
      self.env = env
  
imports: from __future__ import annotations
  from .algorithms import PolicyGradient
label_desc: Use image node_id 1 to create a new DRLAgent object  with example: obj = DRLAgent(env)