code: def main():
      dqn = DeepQNetwork(
          env_name="CartPole-v1",
          epsilon=0.9,
          gamma=0.8,
          decay_rate=0.005,
          min_epsilon=0.1,
      )
  
      # Model builder
      def model(n_inputs, n_outputs):
          clf = NeuralNetwork(optimizer=Adam(), loss=SquareLoss)
          clf.add(Dense(64, input_shape=(n_inputs,)))
          clf.add(Activation("relu"))
          clf.add(Dense(n_outputs))
          return clf
  
      dqn.set_model(model)
  
      print()
      dqn.model.summary(name="Deep Q-Network")
  
      dqn.train(n_epochs=500)
      dqn.play(n_epochs=100)
  
file_path: ML-From-Scratch/mlfromscratch/examples/deep_q_network.py
imports: from __future__ import print_function
  import numpy
  from mlfromscratch.utils import to_categorical
  from mlfromscratch.deep_learning.optimizers import Adam
  from mlfromscratch.deep_learning.loss_functions import SquareLoss
  from mlfromscratch.deep_learning.layers import Dense, Dropout, Flatten, Activation, Reshape, BatchNormalization
  from mlfromscratch.deep_learning import NeuralNetwork
  from mlfromscratch.reinforcement_learning import DeepQNetwork
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: clf