text: import numpy as np
  import gym
  
  from keras.models import Sequential
  from keras.layers import Dense, Activation, Flatten
  from keras.optimizers import Adam
  
  from rl.agents.dqn import DQNAgent
  from rl.policy import BoltzmannQPolicy
  from rl.memory import SequentialMemory
  
  
  ENV_NAME = 'CartPole-v0'
  
  
  # Get the environment and extract the number of actions.
  env = gym.make(ENV_NAME)
  np.random.seed(123)
  env.seed(123)
  nb_actions = env.action_space.n
  
  # Next, we build a very simple model.
  model = Sequential()
  model.add(Flatten(input_shape=(1,) + env.observation_space.shape))
  model.add(Dense(16))
  model.add(Activation('relu'))
  model.add(Dense(16))
  model.add(Activation('relu'))
  model.add(Dense(16))
  model.add(Activation('relu'))
  model.add(Dense(nb_actions))
  model.add(Activation('linear'))
  print(model.summary())
  
  # Finally, we configure and compile our agent. You can use every built-in Keras optimizer and
  # even the metrics!
  memory = SequentialMemory(limit=50000, window_length=1)
  policy = BoltzmannQPolicy()
  dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=10,
                 target_model_update=1e-2, policy=policy)
  dqn.compile(Adam(lr=1e-3), metrics=['mae'])
  
  # Okay, now it's time to learn something! We visualize the training here for show, but this
  # slows down training quite a lot. You can always safely abort the training prematurely using
  # Ctrl + C.
  dqn.fit(env, nb_steps=50000, visualize=True, verbose=2)
  
  # After training is done, we save the final weights.
  dqn.save_weights('dqn_{}_weights.h5f'.format(ENV_NAME), overwrite=True)
  
  # Finally, evaluate our algorithm for 5 episodes.
  dqn.test(env, nb_episodes=5, visualize=True)