- Step1: Install via pip: pip install dataenvgym
- Step2: Import the library and choose an environment: from dataenvgym import DataCleaningEnv
- Step3: Instantiate and configure: env = DataCleaningEnv(config)
- Step4: Create or import an RL agent compatible with Gym
- Step5: Run the training loop: for episode in range(n): obs = env.reset(); done = False; while not done: action = agent.act(obs); obs, reward, done, info = env.step(action)
- Step6: Evaluate and log results using built-in benchmarking tools
- Step7: Customize or combine environments for advanced data pipeline simulations