code: def ddpg_pendulum(ctxt=None, seed=1):
      """Train DDPG with InvertedDoublePendulum-v2 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(snapshot_config=ctxt) as trainer:
          env = GymEnv("InvertedDoublePendulum-v2")
  
          policy = ContinuousMLPPolicy(
              env_spec=env.spec,
              hidden_sizes=[64, 64],
              hidden_nonlinearity=tf.nn.relu,
              output_nonlinearity=tf.nn.tanh,
          )
  
          exploration_policy = AddOrnsteinUhlenbeckNoise(
              env.spec, policy, sigma=0.2
          )
  
          qf = ContinuousMLPQFunction(
              env_spec=env.spec,
              hidden_sizes=[64, 64],
              hidden_nonlinearity=tf.nn.relu,
          )
  
          replay_buffer = PathBuffer(capacity_in_transitions=int(1e6))
  
          sampler = LocalSampler(
              agents=exploration_policy,
              envs=env,
              max_episode_length=env.spec.max_episode_length,
              is_tf_worker=True,
              worker_class=FragmentWorker,
          )
  
          ddpg = DDPG(
              env_spec=env.spec,
              policy=policy,
              policy_lr=1e-4,
              qf_lr=1e-3,
              qf=qf,
              replay_buffer=replay_buffer,
              sampler=sampler,
              steps_per_epoch=20,
              target_update_tau=1e-2,
              n_train_steps=50,
              discount=0.9,
              min_buffer_size=int(1e4),
              exploration_policy=exploration_policy,
              policy_optimizer=tf.compat.v1.train.AdamOptimizer,
              qf_optimizer=tf.compat.v1.train.AdamOptimizer,
          )
  
          trainer.setup(algo=ddpg, env=env)
  
          trainer.train(n_epochs=500, batch_size=100)
  
file_path: garage/src/garage/examples/tf/ddpg_pendulum.py
imports: import tensorflow
  from garage import wrap_experiment
  from garage.envs import GymEnv
  from garage.experiment.deterministic import set_seed
  from garage.np.exploration_policies import AddOrnsteinUhlenbeckNoise
  from garage.replay_buffer import PathBuffer
  from garage.sampler import FragmentWorker, LocalSampler
  from garage.tf.algos import DDPG
  from garage.tf.policies import ContinuousMLPPolicy
  from garage.tf.q_functions import ContinuousMLPQFunction
  from garage.trainer import TFTrainer
label_desc: Use image node_id 1 for calling a global function with example usage: ddpg_pendulum(ctxt, seed) without return types