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{
"model_type": "MultiModalHackVAE",
"framework": "PyTorch",
"task": "representation-learning",
"dataset": "NetHack Learning Dataset",
"latent_dim": 96,
"lowrank_dim": 0,
"architecture": "Multi-modal Variational Autoencoder for NetHack game states",
"author": "Xu Chen",
"description": "Advanced NetHack VAE",
"tags": [
"nethack",
"reinforcement-learning",
"multimodal",
"world-modeling",
"vae"
],
"use_cases": [
"Game state representation learning",
"RL agent state abstraction",
"NetHack gameplay analysis"
],
"training_config": {
"epochs": 15,
"batch_size": 32,
"max_learning_rate": 0.001,
"sequence_size": 32,
"shuffle_batches": true,
"shuffle_within_batch": true,
"vae_config": {
"latent_dim": 96,
"encoder_dropout": 0.1,
"decoder_dropout": 0.1,
"initial_mi_beta": 1.0,
"final_mi_beta": 1.0,
"mi_beta_shape": "constant",
"initial_tc_beta": 0.0,
"final_tc_beta": 10.0,
"tc_beta_shape": "custom",
"initial_dw_beta": 0.2,
"final_dw_beta": 1.0,
"dw_beta_shape": "custom",
"warmup_epoch_ratio": 0.2,
"free_bits": 0.75,
"focal_loss_alpha": 0.5,
"focal_loss_gamma": 2.0
},
"adaptive_weighting": {
"initial_mi_beta": 1.0,
"final_mi_beta": 1.0,
"mi_beta_shape": "constant",
"initial_tc_beta": 0.0,
"final_tc_beta": 10.0,
"tc_beta_shape": "custom",
"initial_dw_beta": 0.2,
"final_dw_beta": 1.0,
"dw_beta_shape": "custom",
"warmup_epoch_ratio": 0.2
},
"regularization": {
"encoder_dropout": 0.1,
"decoder_dropout": 0.1,
"free_bits": 0.75,
"focal_loss_alpha": 0.5,
"focal_loss_gamma": 2.0
},
"early_stopping": {
"enabled": false,
"patience": 3,
"min_delta": 0.01,
"triggered": false,
"best_epoch": null
}
},
"final_train_loss": 980.7457598876953,
"final_test_loss": 990.1863067626953,
"best_train_loss": 980.7457598876953,
"best_test_loss": 985.553515625,
"total_epochs": 15
} |