config_annotation.json

{
  "env_paras": {
    "num_jobs": "Number of jobs, taking effect during training",
    "num_mas": "Number of machines, taking effect during training",
    "batch_size": "Number of parallel instances during training",
    "ope_feat_dim" : "Dimension of the raw feature vectors of operation nodes",
    "ma_feat_dim" : "Dimension of the raw feature vectors of machine nodes",
    "show_mode": "Result display mode (deprecated in the final experiment)",
    "valid_batch_size": "Number of instances in single validation set"
  },
  "model_paras": {
    "in_size_ma": "Dimension of the raw feature vectors of machine nodes",
    "out_size_ma": "Dimension of the embedding of machine nodes",
    "in_size_ope": "Dimension of the raw feature vectors of operation nodes",
    "out_size_ope": "Dimension of the embedding of operation nodes",
    "hidden_size_ope": "Hidden dimensions of the MLPs",
    "num_heads": "len() means of the number of HGNN iterations, and the element means the number of heads of each HGNN (=1 in final experiment)",
    "dropout": "dropout ratio",
    "n_latent_actor": "Hidden dimensions of the actor",
    "n_latent_critic": "Hidden dimensions of the critic",
    "n_hidden_actor": "Number of layers in actor",
    "n_hidden_critic": "Number of layers in critic",
    "action_dim": "Output dimension of actor"
  },
  "train_paras": {
    "lr": "learning rate",
    "betas": "paras for Adam (default value)",
    "gamma": "discount factor",
    "K_epochs": "Update policy for K epochs",
    "eps_clip": "clip ratio for PPO",
    "A_coeff": "coefficient for policy loss",
    "vf_coeff": "coefficient for value loss",
    "entropy_coeff": "coefficient for entropy term",
    "max_iterations": "Maximum number of iterations for training",
    "save_timestep": "Number of iterations to validate and save the network",
    "update_timestep": "Number of iterations to update the network",
    "viz": "Turn on visdom to visualize the training process",
    "viz_name": "Name of this visdom window",
    "minibatch_size": "batch size for updating",
    "parallel_iter": "Number of iterations to replace training instances"
  },
  "test_paras": {
    "num_ins": "Number of instances in this test set",
    "rules": "The rule used to test",
    "sample": "Turn on the DRL-S mode",
    "num_sample": "Number of copied instances to sample for DRL-S",
    "num_average": "Number of loops in DRL-G to calculate the mean",
    "public_ins": "Instances from well-known benchmarks or not",
    "data_path": "Relative path of this test set"
  }
}