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 DRL_validation(
      model,
      test_env,
      policy=None,
      online_training_period=10,
      learning_rate=None,
      optimizer=None,
  ):
      model.test(
          test_env,
          policy,
          online_training_period,
          learning_rate,
          optimizer,
      )
  
imports: from __future__ import annotations
  from .algorithms import PolicyGradient
label_desc: Use image node_id 4 for calling the DRLAgent obj's underlying member method code with example usage: obj.DRL_validation(model, test_env, policy, online_training_period, learning_rate, optimizer) without return types