Usage

python

import torch
from training.online_rl import train_online_ppo_with_pretrained_models

# Load the complete model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Load individual components
ppo_checkpoint = torch.load('ppo_policy.pth', map_location=device)
vae_data = torch.load('vae_model.pth', map_location=device)
hmm_data = torch.load('hmm_model.pth', map_location=device)

# Use for inference or continued training
results = train_online_ppo_with_pretrained_models(
    env_name="MiniHack-Room-5x5-v0",
    vae_repo_id="CatkinChen/nethack-vae-hmm",
    hmm_repo_id="CatkinChen/nethack-hmm",
    test_mode=True
)

Training Configuration

Environment: MiniHack-Room-5x5-v0
Learning Rate: 0.0005
Batch Size: 32
Training Time: 0.02 seconds
Device: cuda
Seed: None

Performance

Training completed successfully with the following configuration:

Curiosity-driven exploration: True
Random Network Distillation: False

Generated on: 2025-09-19 14:38:50

CatkinChen/nethack-ppo-ablation-baseline

CatkinChen/nethack-ppo-ablation-baseline

Model Components

1. PPO Policy (ppo_policy.pth)

2. VAE Model (vae_model.pth)

3. HMM Model (hmm_model.pth)

Usage

Training Configuration

Performance

CatkinChen/
nethack-ppo-ablation-baseline

1. PPO Policy (`ppo_policy.pth`)

2. VAE Model (`vae_model.pth`)

3. HMM Model (`hmm_model.pth`)