code: def dqn_cartpole(ctxt=None, seed=1):
      """Train TRPO with CubeCrash-v0 environment.
  
      Args:
          ctxt (garage.experiment.ExperimentContext): The experiment
              configuration used by Trainer to create the snapshotter.
          seed (int): Used to seed the random number generator to produce
              determinism.
  
      """
      set_seed(seed)
      with TFTrainer(ctxt) as trainer:
          n_epochs = 10
          steps_per_epoch = 10
          sampler_batch_size = 500
          num_timesteps = (
              n_epochs * steps_per_epoch * sampler_batch_size
          )
          env = GymEnv("CartPole-v0")
          replay_buffer = PathBuffer(capacity_in_transitions=int(1e4))
          qf = DiscreteMLPQFunction(
              env_spec=env.spec, hidden_sizes=(64, 64)
          )
          policy = DiscreteQFArgmaxPolicy(env_spec=env.spec, qf=qf)
          exploration_policy = EpsilonGreedyPolicy(
              env_spec=env.spec,
              policy=policy,
              total_timesteps=num_timesteps,
              max_epsilon=1.0,
              min_epsilon=0.02,
              decay_ratio=0.1,
          )
  
          sampler = LocalSampler(
              agents=exploration_policy,
              envs=env,
              max_episode_length=env.spec.max_episode_length,
              is_tf_worker=True,
              worker_class=FragmentWorker,
          )
  
          algo = DQN(
              env_spec=env.spec,
              policy=policy,
              qf=qf,
              exploration_policy=exploration_policy,
              replay_buffer=replay_buffer,
              sampler=sampler,
              steps_per_epoch=steps_per_epoch,
              qf_lr=1e-4,
              discount=1.0,
              min_buffer_size=int(1e3),
              double_q=True,
              n_train_steps=500,
              target_network_update_freq=1,
              buffer_batch_size=32,
          )
  
          trainer.setup(algo, env)
          trainer.train(
              n_epochs=n_epochs, batch_size=sampler_batch_size
          )
  
file_path: garage/src/garage/examples/tf/dqn_cartpole.py
imports: from garage import wrap_experiment
  from garage.envs import GymEnv
  from garage.experiment.deterministic import set_seed
  from garage.np.exploration_policies import EpsilonGreedyPolicy
  from garage.replay_buffer import PathBuffer
  from garage.sampler import FragmentWorker, LocalSampler
  from garage.tf.algos import DQN
  from garage.tf.policies import DiscreteQFArgmaxPolicy
  from garage.tf.q_functions import DiscreteMLPQFunction
  from garage.trainer import TFTrainer
label_desc: Use image node_id 1 for calling a global function with example usage: dqn_cartpole(ctxt, seed) without return types