text: from design_baselines.cbas import cbas
  from design_baselines.rep import rep
  from design_baselines.rep_coms_cleaned import coms_cleaned
  seed = [1]
  mmd_param = [1000]
  d_list = [32] #[16, 32, 64, 128, 256]
  name = "ant"
  task = "AntMorphology-Exact-v0"
  for s in seed:
      for m in mmd_param:
          for d in d_list:
              coms_cleaned(logging_dir = "/nfs/kun2/users/hanqi2019/anttest/1218"+name+"rep"+str(d)+"seed"+str(s)+"mmd"+str(m),
                                      task=task,
                                      task_relabel=True,
                                      normalize_ys=True,
                                      normalize_xs=True,
                                      in_latent_space=False,
                                      vae_hidden_size=64,
                                      vae_latent_size=256,
                                      vae_activation='relu',
                                      vae_kernel_size=3,
                                      vae_num_blocks=4,
                                      vae_lr=0.0003,
                                      vae_beta=1.0,
                                      vae_batch_size=32,
                                      vae_val_size=200,
                                      vae_epochs=10,
                                      particle_lr=0.05,
                                      particle_train_gradient_steps=50,
                                      particle_evaluate_gradient_steps=50,
                                      particle_entropy_coefficient=0.0,
                                      forward_model_activations=['relu', 'relu', 'relu', 'relu'],
                                      forward_model_hidden_size=2048,
                                      forward_model_final_tanh=False,
                                      forward_model_lr=0.0003,
                                      forward_model_alpha=0.1,
                                      forward_model_alpha_lr=0.01,
                                      forward_model_overestimation_limit=0.5,
                                      forward_model_noise_std=0.0,
                                      forward_model_batch_size=128,
                                      forward_model_val_size=200,
                                      forward_model_epochs=300,
                                      evaluation_samples=128,
                                      fast=True,
                                      latent_space_size=[d,1],
                                      rep_model_activations=['relu', 'relu'],
                                      rep_model_lr=0.0003,
                                      rep_model_hidden_size=2048,
                                      policy_model_lr=0.0003,
                                      noise_input = [1, 10],
                                      mmd_param = m,
                                      seed = s
                          )