class_docstr: Implementations for DRL algorithms
  
  Attributes
  ----------
      env: gym environment class
          user-defined class
      price_array: numpy array
          OHLC data
      tech_array: numpy array
          techical data
      turbulence_array: numpy array
          turbulence/risk data
  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
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code: def __init__(self, env, price_array, tech_array, turbulence_array):
      self.env = env
      self.price_array = price_array
      self.tech_array = tech_array
      self.turbulence_array = turbulence_array
  
file_path: FinRL/finrl/agents/rllib/models.py
imports: from __future__ import annotations
  import ray
  from ray.rllib.algorithms.a2c import a2c
  from ray.rllib.algorithms.ddpg import ddpg
  from ray.rllib.algorithms.ppo import ppo
  from ray.rllib.algorithms.sac import sac
  from ray.rllib.algorithms.td3 import td3
label_desc: Use image node_id 1 to create a new DRLAgent object  with example: obj = DRLAgent(env, price_array, tech_array, turbulence_array)