code: def main():
      # Create the experiment
      experiment.create(name="dqn")
  
      # Configurations
      configs = {
          # Number of updates
          "updates": 1_000_000,
          # Number of epochs to train the model with sampled data.
          "epochs": 8,
          # Number of worker processes
          "n_workers": 8,
          # Number of steps to run on each process for a single update
          "worker_steps": 4,
          # Mini batch size
          "mini_batch_size": 32,
          # Target model updating interval
          "update_target_model": 250,
          # Learning rate.
          "learning_rate": FloatDynamicHyperParam(1e-4, (0, 1e-3)),
      }
  
      # Configurations
      experiment.configs(configs)
  
      # Initialize the trainer
      m = Trainer(**configs)
      # Run and monitor the experiment
      with experiment.start():
          m.run_training_loop()
      # Stop the workers
      m.destroy()
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/rl/dqn/experiment.py
imports: import numpy
  import torch
  from labml import tracker, experiment, logger, monit
  from labml.internal.configs.dynamic_hyperparam import FloatDynamicHyperParam
  from labml_helpers.schedule import Piecewise
  from labml_nn.rl.dqn import QFuncLoss
  from labml_nn.rl.dqn.model import Model
  from labml_nn.rl.dqn.replay_buffer import ReplayBuffer
  from labml_nn.rl.game import Worker
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types