Add MARL Infiltration Risk Assessment Model and dataset 📊🤖

README.md

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+# MARL Infiltration Risk Assessment Model
+
+A Multi-Agent Reinforcement Learning (MARL) model for infiltration risk assessment in security scenarios.
+
+[Preview](https://app.isildur.co)
+
+- **Multi-agent architecture**: Defender and adversary agents
+- **Enhanced Data**: Synthetic scenarios LLM-enhanced (should be real historical data for production use)
+- **Features**: Terrain, weather, target types, infiltration methods, and countermeasures, etc 
+
+
+![Training Metrics](images/training_metrics.png)
+
+- **Python**: 3.8+
+- **Dependencies**: PyTorch 2.0+, NumPy, Matplotlib
+- **Hardware**: CPU compatible, GPU recommended
+
+## Citation
+
+```
+@software{marl_infiltration_risk_model,
+  author = {Isildur},
+  title = {MARL Infiltration Mission Risk Assessment Model},
+  year = {2025},
+  url = {https://huggingface.co/spatial-ai/marl-infiltration-risk}
+}
+```

config.json

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+{
+  "model_name": "MARL Infiltration Risk Assessment Model",
+  "version": "1.0.0",
+  "date": "2025-03-26",
+  "architecture": {
+    "type": "multi_agent_reinforcement_learning",
+    "agents": ["defender", "adversary"],
+    "state_dim": 40,
+    "action_dim": 10,
+    "hidden_dim": 128
+  },
+  "training": {
+    "dataset_size": 20000,
+    "llm_enhanced_examples": 1000,
+    "llm_model": "deepseek-r1-distill-llama-70b-specdec",
+    "epochs": 100,
+    "batch_size": 32,
+    "learning_rate": 0.001,
+    "final_metrics": {
+      "defender_accuracy": 0.72,
+      "adversary_accuracy": 0.73,
+      "defender_loss": 0.93,
+      "adversary_loss": 0.91
+    }
+  },
+  "features": {
+    "terrain_types": ["urban", "mountain", "coastal", "desert", "forest"],
+    "weather_conditions": ["clear", "cloudy", "rain", "fog", "sandstorm", "snow"],
+    "target_types": ["military", "industrial", "infrastructure", "government", "civilian"],
+    "infiltration_methods": ["ground", "aerial", "maritime", "underground", "cyber"],
+    "equipment_options": [
+      "night_vision", "thermal_imaging", "communication_devices", "weapons", 
+      "explosives", "hacking_tools", "climbing_gear", "diving_gear", "disguises",
+      "drones", "vehicles", "signal_jammers"
+    ],
+    "countermeasure_options": [
+      "guards", "cameras", "motion_sensors", "alarms", "barriers", "biometric_access",
+      "patrols", "drones", "dogs", "facial_recognition", "signal_jammers"
+    ]
+  },
+  "formats": {
+    "pytorch": "model.pt",
+    "onnx": "model.onnx"
+  },
+  "license": "MIT",
+  "repository": "https://huggingface.co/spatial-ai/marl-infiltration-risk"
+}

model/marl_20250326_192758/adversary.onnx

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+oid sha256:48440f464c15bdf045b89b30e76abd81c9a0fd234f8f797318d24d874cdf2cd3
+size 89948086

model/marl_20250326_192758/config.json

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+{
+  "name": "marl_world_model_20250326_192758",
+  "version": "1.0",
+  "created_at": "2025-03-26T19:27:59.105505",
+  "model_type": "MARL",
+  "architecture": {
+    "state_dim": 45,
+    "action_dim": 16,
+    "hidden_dim": 1024
+  },
+  "files": {
+    "pytorch_model": "model.pt",
+    "defender_onnx": "defender.onnx",
+    "adversary_onnx": "adversary.onnx"
+  },
+  "source": {
+    "original_model": "marl_model_20250326_192235.pt"
+  }
+}

model/marl_20250326_192758/defender.onnx

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+size 89948076

model/marl_20250326_192758/model.pt

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+size 260008516