text: import os
  # from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
  # from rllab.envs.mujoco.gather.swimmer_gather_env import SwimmerGatherEnv
  # os.environ["THEANO_FLAGS"] = "device=cpu"
  # from rllab.envs.gym_env import GymEnv
  # from rllab.envs.normalized_env import normalize
  
  # from rllab.policies.gaussian_mlp_policy import GaussianMLPPolicy
  # from rllab.envs.normalized_env import NormalizedEnv
  
  # from rllab.algos.trpo import TRPO
  # from rllab.misc.instrument import stub, run_experiment_lite
  import itertools
  
  from sandbox.rocky.tf.algos.im import Imitate
  from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
  from rllab.envs.box2d.cartpole_env import CartpoleEnv
  from rllab.envs.gym_env import GymEnv
  from rllab.envs.normalized_env import normalize
  from sandbox.rocky.tf.optimizers.conjugate_gradient_optimizer import ConjugateGradientOptimizer
  from sandbox.rocky.tf.optimizers.conjugate_gradient_optimizer import FiniteDifferenceHvp
  from sandbox.rocky.tf.policies.egreedyforward import EGreedyPolicy
  from sandbox.rocky.tf.envs.base import TfEnv
  from rllab.misc.instrument import stub, run_experiment_lite
  
  # stub(globals())
  
  # Param ranges
  seeds = range(1, 10)
  # SwimmerGather hierarchical task
  # mdp_classes = [SwimmerGatherEnv]
  # mdps = [NormalizedEnv(env=mdp_class())
  #         for mdp_class in mdp_classes]
  
  env = TfEnv(normalize(GymEnv("Pusher3DOF-v1")))
  
  for seed in seeds:
  
      # policy = GaussianMLPPolicy(
      #     env_spec=env.spec,
      #     hidden_sizes=(32, 32),#, 64),
      #     init_std=10
      # )
  
      # baseline = LinearFeatureBaseline(
      #     env.spec,
      # )
  
      # batch_size = 25000
      # algo = TRPO(
      #     env=mdp,
      #     policy=policy,
      #     baseline=baseline,
      #     batch_size=batch_size,
      #     whole_paths=True,
      #     max_path_length=50,
      #     n_itr=10000,
      #     step_size=0.01,
      #     subsample_factor=1.0,
      # )
      policy = EGreedyPolicy(
          env_spec=env.spec
      )
      algo = Imitate(
          env=env,
          policy=policy,
          baseline=None,
      )
  
      run_experiment_lite(
          algo.train(),
          exp_prefix="forward_test",
          n_parallel=6,
          snapshot_mode="all",
          seed=seed,
          mode="local"
      )
  