text: from rllab.baselines.linear_feature_baseline import LinearFeatureBaseline
  from rllab.envs.normalized_env import normalize
  from sandbox.rocky.tf.envs.base import TfEnv
  from sandbox.rocky.tf.policies.gaussian_mlp_policy import GaussianMLPPolicy
  from sandbox.rocky.tf.algos.trpo import TRPO
  from rllab.misc.instrument import stub, run_experiment_lite
  #from rllab.envs.gym_env import GymEnv
  #from rllab.envs.mujoco.swimmer_randgoal_env import SwimmerRandGoalEnv
  from rllab.envs.mujoco.swimmer_randgoal_oracle_env import SwimmerRandGoalOracleEnv
  import sys
  
  stub(globals())
  
  from rllab.misc.instrument import VariantGenerator, variant
  
  
  class VG(VariantGenerator):
  
      @variant
      def step_size(self):
          return [0.005,0.01,0.02] #, 0.05, 0.1]
  
      @variant
      def seed(self):
          return [2,3] #, 11, 21, 31, 41]
  
  variants = VG().variants()
  
  for v in variants:
  
      env = TfEnv(normalize(SwimmerRandGoalOracleEnv()))
      #env = TfEnv(normalize(GymEnv('HalfCheetah-v1', record_video=False, record_log=False)))
  
      policy = GaussianMLPPolicy(
          env_spec=env.spec,
          # The neural network policy should have two hidden layers, each with 32 hidden units.
          hidden_sizes=(100, 100),
          name="policy"
      )
  
      baseline = LinearFeatureBaseline(env_spec=env.spec)
  
      algo = TRPO(
          env=env,
          policy=policy,
          baseline=baseline,
          batch_size=10000,
          max_path_length=500,
          n_itr=500,
          discount=0.99,
          step_size=v["step_size"],
          # Uncomment both lines (this and the plot parameter below) to enable plotting
          # plot=True,
      )
  
      run_experiment_lite(
          algo.train(),
          exp_prefix="trpo_swimmer_baselines",
          # Number of parallel workers for sampling
          n_parallel=1,
          # Only keep the snapshot parameters for the last iteration
          snapshot_mode="last",
          # Specifies the seed for the experiment. If this is not provided, a random seed
          # will be used
          seed=v["seed"],
          # mode="local",
          mode="ec2",
          variant=v,
          # plot=True,
          # terminate_machine=False,
      )
  