def rlmf_eval():
  """Eval set of hparams for model-free PPO."""
  hparams = rlmf_original()
  hparams.batch_size = 16
  hparams.eval_batch_size = 32
  hparams.eval_episodes_num = 2
  hparams.eval_sampling_temps = [0.5, 0.0, 1.0]
  hparams.eval_rl_env_max_episode_steps = 40000
  hparams.add_hparam("ppo_epoch_length", 128)
  hparams.add_hparam("ppo_optimization_batch_size", 32)
  hparams.add_hparam("ppo_epochs_num", 10000)
  hparams.add_hparam("ppo_eval_every_epochs", 500)
  hparams.add_hparam("attempt", 0)
  hparams.add_hparam("moe_loss_coef", 0)
  return hparams
Eval set of hparams for model-free PPO.
tensor2tensor/models/research/rl.py