def ppo_base_v1():
  """Set of hyperparameters."""
  hparams = common_hparams.basic_params1()
  hparams.learning_rate_schedule = "constant"
  hparams.learning_rate_constant = 1e-4
  hparams.clip_grad_norm = 0.5
  hparams.weight_decay = 0
  # If set, extends the LR warmup to all epochs except the final one.
  hparams.add_hparam("lr_decay_in_final_epoch", False)
  hparams.add_hparam("init_mean_factor", 0.1)
  hparams.add_hparam("init_logstd", 0.1)
  hparams.add_hparam("policy_layers", (100, 100))
  hparams.add_hparam("value_layers", (100, 100))
  hparams.add_hparam("clipping_coef", 0.2)
  hparams.add_hparam("gae_gamma", 0.99)
  hparams.add_hparam("gae_lambda", 0.95)
  hparams.add_hparam("entropy_loss_coef", 0.01)
  hparams.add_hparam("value_loss_coef", 1)
  hparams.add_hparam("optimization_epochs", 15)
  hparams.add_hparam("epoch_length", 200)
  hparams.add_hparam("epochs_num", 2000)
  hparams.add_hparam("eval_every_epochs", 10)
  hparams.add_hparam("save_models_every_epochs", 30)
  hparams.add_hparam("optimization_batch_size", 50)
  hparams.add_hparam("intrinsic_reward_scale", 0.)
  hparams.add_hparam("logits_clip", 0.0)
  hparams.add_hparam("dropout_ppo", 0.1)
  hparams.add_hparam("effective_num_agents", None)
  # TODO(afrozm): Clean this up, this is used in PPO learner to get modalities.
  hparams.add_hparam("policy_problem_name", "dummy_policy_problem")
  return hparams