Add model config
Browse files- config.json +52 -0
config.json
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{
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"model_type": "MultiModalHackVAE",
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"framework": "PyTorch",
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"task": "representation-learning",
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"dataset": "NetHack Learning Dataset",
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"latent_dim": 64,
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"include_inventory": false,
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"architecture": "Multi-modal Variational Autoencoder for NetHack game states",
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"author": "Xu Chen",
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"description": "Advanced NetHack VAE",
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"tags": [
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"nethack",
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"reinforcement-learning",
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"multimodal",
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"world-modeling",
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"vae"
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],
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"use_cases": [
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"Game state representation learning",
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"RL agent state abstraction",
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"NetHack gameplay analysis"
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],
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"metrics": {
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"reconstruction_quality": "High",
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"latent_dim": 256,
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"compression_ratio": "21x79x16 -> 256"
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},
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"training_config": {
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"epochs": 5,
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"batch_size": 32,
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"learning_rate": 0.001,
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"sequence_size": 32,
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"include_inventory": false,
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"adaptive_weighting": {
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"initial_weight_emb": 1.5,
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"final_weight_emb": 1.0,
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"weight_emb_shape": "cubic",
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"initial_weight_raw": 0.2,
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"final_weight_raw": 1.0,
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"weight_raw_shape": "linear",
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"initial_kl_beta": 0.0001,
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"final_kl_beta": 1.0,
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"kl_beta_shape": "cosine",
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"warmup_epoch_ratio": 0.3
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}
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},
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"final_train_loss": 72.08752632141113,
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"final_test_loss": 70.96195983886719,
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"best_train_loss": 42.672746658325195,
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"best_test_loss": 38.52728271484375,
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"total_epochs": 5
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}
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