text: import random
  
  from rlkit.envs.env_utils import get_dim
  from rlkit.exploration_strategies.base import \
      PolicyWrappedWithExplorationStrategy
  from rlkit.launchers.launcher_util import resume_torch_algorithm, \
      continue_experiment, run_experiment
  from rlkit.torch.networks import FeedForwardQFunction, FeedForwardPolicy
  from rlkit.torch.ddpg.ddpg import DDPG
  from rllab.envs.mujoco.half_cheetah_env import HalfCheetahEnv
  from rlkit.exploration_strategies.ou_strategy import OUStrategy
  from rlkit.torch import pytorch_util as ptu
  import sys
  from rllab.envs.normalized_env import normalize
  
  
  def example(variant):
      env_class = variant['env_class']
      env_params = variant['env_params']
      env = env_class(**env_params)
      normalize(env)
      es_class = variant['es_class']
      es_params = dict(
          action_space=env.action_space,
          **variant['es_params']
      )
      use_gpu = variant['use_gpu']
      es = es_class(**es_params)
      qf = FeedForwardQFunction(
          int(env.observation_space.flat_dim),
          int(env.action_space.flat_dim),
          100,
          100,
      )
      policy_class = variant['policy_class']
      policy_params = dict(
          obs_dim=get_dim(env.observation_space),
          action_dim=get_dim(env.action_space),
          fc1_size=100,
          fc2_size=100,
      )
      policy = policy_class(**policy_params)
      exploration_policy = PolicyWrappedWithExplorationStrategy(
          exploration_strategy=es,
          policy=policy,
      )
      algorithm = DDPG(
          env,
          qf=qf,
          policy=policy,
          exploration_policy=exploration_policy,
          **variant['algo_params'],
      )
      if use_gpu and ptu.gpu_enabled():
          algorithm.cuda()
      algorithm.train()
  
  
  experiments = [
      'joint_angle|fixed_angle',
      'joint_angle|varying_angle',
      'end_effector_position|fixed_ee',
      'end_effector_position|varying_ee',
      'end_effector_position_orientation|fixed_ee',
      'end_effector_position_orientation|varying_ee'
  ]
  
  max_path_length = 100
  if __name__ == "__main__":
      try:
          exp_dir = sys.argv[1]
      except:
          exp_dir = None
      if exp_dir:
          continue_experiment(exp_dir, resume_function=resume_torch_algorithm)
      else:
          run_experiment(
              example,
              exp_prefix="ddpg-cheetah-parallel-TEST",
              seed=random.randint(0, 666),
              mode='local',
              variant={
                  'version': 'Original',
                  'max_path_length': max_path_length,
                  'use_gpu': True,
                  'es_class': OUStrategy,
                  'env_class': HalfCheetahEnv,
                  'policy_class': FeedForwardPolicy,
                  'normalize_env': False,
                  'env_params': dict(),
                  'es_params': {
                      'max_sigma': .25,
                      'min_sigma': .25,
                  },
                  'algo_params': dict(
                      num_epochs=100,
                      num_steps_per_epoch=10000,
                      num_steps_per_eval=100,
                      use_soft_update=True,
                      tau=1e-2,
                      batch_size=128,
                      max_path_length=1000,
                      discount=0.99,
                      qf_learning_rate=1e-3,
                      policy_learning_rate=1e-4,
                      collection_mode='online',
                  ),
              },
              use_gpu=True,
          )
          # variant = {
          #         'version': 'Original',
          #         'max_path_length': max_path_length,
          #         'use_gpu': True,
          #         'es_class': OUStrategy,
          #         'env_class': HalfCheetahEnv,
          #         'policy_class': FeedForwardPolicy,
          #         'normalize_env': False,
          #         'env_params': dict(),
          #         'es_params':{
          #             'max_sigma': .25,
          #             'min_sigma': .25,
          #         },
          #         'algo_params':dict(
          #             num_epochs=100,
          #             num_steps_per_epoch=10000,
          #             num_steps_per_eval=100,
          #             use_soft_update=True,
          #             tau=1e-2,
          #             batch_size=128,
          #             max_path_length=1000,
          #             discount=0.99,
          #             qf_learning_rate=1e-3,
          #             policy_learning_rate=1e-4,
          #             collection_mode='online-parallel',
          #             env_train_ratio=10,
          #         ),
          #     }
          # search_space = {
          #     'algo_params.env_train_ratio':[10, 20, 30, 40, 50]
          # }
          # sweeper = hyp.DeterministicHyperparameterSweeper(
          #     search_space, default_parameters=variant,
          # )
          # for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()):
          #     run_experiment(
          #         example,
          #         exp_prefix="ddpg-cheetah-cheetah-relative-ratios",
          #         seed=random.randint(0, 666),
          #         mode='ec2',
          #         variant=variant,
          #         exp_id=exp_id,
          #     )
  