text: """
  AWR + SAC from demo experiment
  """
  
  from rlkit.launchers.experiments.awac.awac_rl import experiment
  
  import rlkit.misc.hyperparameter as hyp
  from rlkit.launchers.arglauncher import run_variants
  
  from rlkit.torch.sac.policies import GaussianPolicy
  
  if __name__ == "__main__":
      variant = dict(
          num_epochs=3000,
          num_eval_steps_per_epoch=5000,
          num_trains_per_train_loop=1000,
          num_expl_steps_per_train_loop=1000,
          min_num_steps_before_training=1000,
          max_path_length=1000,
          batch_size=1024,
          replay_buffer_size=int(1E6),
  
          layer_size=256,
          policy_class=GaussianPolicy,
          policy_kwargs=dict(
              hidden_sizes=[256, 256, ],
          ),
  
          algorithm="SAC",
          version="normal",
          collection_mode='batch',
          trainer_kwargs=dict(
              discount=0.99,
              soft_target_tau=5e-3,
              target_update_period=1,
              policy_lr=3E-4,
              qf_lr=3E-4,
              reward_scale=1,
              beta=1,
              use_automatic_entropy_tuning=True,
              alpha=0,
              compute_bc=False,
  
              use_awr_update=True,
              use_reparam_update=True,
              reparam_weight=1.0,
              awr_weight=0.0,
  
              # bc_num_pretrain_steps=0,
              # q_num_pretrain_steps=0,
          ),
          num_exps_per_instance=1,
          region='us-west-1',
  
          logger_variant=dict(
              tensorboard=True,
          ),
          load_demos=False,
          pretrain_policy=False,
          pretrain_rl=False,
      )
  
      search_space = {
          'env': [
              'half-cheetah',
              'inv-double-pendulum',
              'pendulum',
              'ant',
              'walker',
              'hopper',
              'humanoid',
              'swimmer',
          ],
          # 'trainer_kwargs.bc_loss_type': ["mse"],
          # 'trainer_kwargs.awr_loss_type': ["mle"],
          'seedid': range(3),
          # 'trainer_kwargs.beta': [0.1, 1, 10],
          'trainer_kwargs.use_automatic_entropy_tuning': [False, True],
      }
  
      sweeper = hyp.DeterministicHyperparameterSweeper(
          search_space, default_parameters=variant,
      )
  
      variants = []
      for variant in sweeper.iterate_hyperparameters():
          variants.append(variant)
  
      run_variants(experiment, variants, run_id=0)
  