⚡ Ghost Unit RL Core · Where AI Learns to Strike: 31‑Dimensional State Space · 4 Action Policies · Immutable Model Registry on IPFS

🧠 RL Models Directory · Ghost Unit AI

Reinforcement Learning for Optimal Trend Detection & Strike Execution

📌 Clarification: The rl-models/ directory contains trained reinforcement learning models for AI-driven trend detection and strike execution. The trained-policy.onnx file is the exported, production-ready neural network policy that runs in the browser via ONNX Runtime Web or on the backend.

📁 RL Models Directory Structure

rl-models/
├── trained-policy.onnx          # 🎯 Exported ONNX model for trend prediction
├── trained-policy_metadata.json # 📋 Model metadata (version, accuracy, etc.)
├── training/
│   ├── train_trend_detector.py  # 🚀 Main training script
│   ├── environment.py           # 🎮 Market simulation environment
│   ├── reward_function.py       # 🎁 Custom reward logic
│   └── config.yaml              # ⚙️ Hyperparameters
├── models/
│   ├── policy_network.py        # 🧠 Neural network architecture
│   ├── dqn_agent.py             # 🤖 Deep Q-Network agent
│   └── ppo_agent.py             # 📈 Proximal Policy Optimization
├── inference/
│   ├── onnx_runtime.py          # 🔮 ONNX inference wrapper (Python)
│   ├── browser_inference.js     # 🌐 WebAssembly/ONNX for frontend
│   └── model_loader.py          # 📦 Dynamic model loading
├── export/
│   └── export_to_onnx.py        # 🔄 PyTorch → ONNX converter
├── trend-detector/              # 📊 RL agent for detecting emerging trends
│   ├── policy.pth               # 🔬 PyTorch trained weights
│   ├── config.yaml              # ⚙️ Hyperparameters
│   └── feature_extractor.onnx   # 🔧 Feature engineering module
├── strike-optimizer/            # ⚡ RL for optimal strike timing
│   ├── dqn_policy.onnx          # 🎯 Deep Q-Network for strike decisions
│   ├── reward_function.py       # 🎁 Custom reward logic
│   └── environment.py           # 🎮 Market simulation env
├── anomaly-scout/               # 🕵️ Unsupervised RL for anomaly detection
│   ├── isolation_forest.onnx    # 🌲 Hybrid model
│   └── threshold.pkl            # 📊 Dynamic thresholds
├── ensembles/                   # 🔄 Model blending
│   └── weighted_ensemble.onnx   # ⚖️ Combined predictions
├── checkpoints/                 # 💾 Training checkpoints
│   └── checkpoint_ep*.pt        # 🔄 Periodic model saves
└── ipfs_storage/                # 📀 Immutable model storage
    └── model_versioning.py      # 🧬 IPFS model versioning

🎯 What the Trained Policy Does

The trained-policy.onnx file is the exported, production-ready neural network policy that:

• Takes real-time market data as input (31 features)
• Outputs: { action: "strike" | "wait" | "pause", confidence: 0.0-1.0 }
• Runs in the browser via ONNX Runtime Web or on the backend
• Is immutable and versioned on IPFS for auditability

1️⃣ Core RL Environment

📄 rl-models/training/environment.py

rl-models/training/environment.py

import numpy as np
from gym import Env, spaces

class HealthTrendMarketEnv(Env):
    """
    Market simulation environment for RL agent to learn optimal strike timing.
    
    State Space (observation): 31-dimensional continuous space
    - price_momentum: 10 recent price changes
    - volume_profile: 10 recent volume indicators
    - social_sentiment: 5 sentiment scores
    - competitor_activity: 3 competitor strike counts
    - temporal_features: hour, day_of_week, seasonality
    
    Action Space: 4 discrete actions
    - 0: WAIT (do nothing, continue observing)
    - 1: STRIKE (execute a health trend strike)
    - 2: AGGRESSIVE_STRIKE (double down, higher risk/reward)
    - 3: PAUSE (emergency cooldown, negative reward)
    """
    
    def __init__(self, config=None):
        super().__init__()
        self.observation_space = spaces.Box(low=-np.inf, high=np.inf, shape=(31,))
        self.action_space = spaces.Discrete(4)
        self.balance = 10000
        self.strike_history = []
        
    def step(self, action):
        reward = self._calculate_reward(action)
        observation = self._get_observation()
        done = self.balance 

2️⃣ PPO Agent

📄 rl-models/models/ppo_agent.py

rl-models/models/ppo_agent.py

import torch
import torch.nn as nn

class PolicyNetwork(nn.Module):
    def __init__(self, state_dim=31, action_dim=4):
        super().__init__()
        self.shared = nn.Sequential(
            nn.Linear(state_dim, 256), nn.ReLU(),
            nn.Linear(256, 128), nn.ReLU(),
            nn.Linear(128, 64), nn.ReLU()
        )
        self.actor = nn.Sequential(
            nn.Linear(64, 32), nn.ReLU(),
            nn.Linear(32, action_dim), nn.Softmax(dim=-1)
        )
        self.critic = nn.Sequential(
            nn.Linear(64, 32), nn.ReLU(),
            nn.Linear(32, 1)
        )
        
    def forward(self, state):
        features = self.shared(state)
        return self.actor(features), self.critic(features)

class PPOAgent:
    def __init__(self):
        self.policy = PolicyNetwork()
        self.optimizer = torch.optim.Adam(self.policy.parameters(), lr=3e-4)
        
    def get_action(self, state):
        with torch.no_grad():
            action_probs, _ = self.policy(torch.FloatTensor(state).unsqueeze(0))
        return torch.multinomial(action_probs, 1).item()

3️⃣ Training Pipeline

📄 rl-models/training/train_trend_detector.py

rl-models/training/train_trend_detector.py

import yaml
from tqdm import tqdm

class TrainingPipeline:
    def __init__(self, config_path='config.yaml'):
        with open(config_path, 'r') as f:
            self.config = yaml.safe_load(f)
        self.env = HealthTrendMarketEnv()
        self.agent = PPOAgent()
        
    def train(self, num_episodes=1000):
        for episode in tqdm(range(num_episodes)):
            state = self.env.reset()
            episode_reward = 0
            while True:
                action = self.agent.get_action(state)
                next_state, reward, done, _ = self.env.step(action)
                episode_reward += reward
                if done:
                    break
            if episode % 100 == 0:
                print(f"📊 Episode {episode}: Reward = {episode_reward:.2f}")
        return self.agent
    
    def export_to_onnx(self, output_path='../exported/trained-policy.onnx'):
        import torch.onnx
        self.agent.policy.eval()
        dummy_input = torch.randn(1, 31)
        torch.onnx.export(self.agent.policy, dummy_input, output_path,
                          input_names=['market_state'],
                          output_names=['action_probs', 'state_value'],
                          opset_version=11)
        print(f"✅ Model exported to ONNX: {output_path}")

4️⃣ ONNX Inference · Browser Integration

📄 rl-models/inference/browser_inference.js

rl-models/inference/browser_inference.js

import * as ort from 'onnxruntime-web';

class GhostUnitInference {
    constructor() {
        this.session = null;
        this.actionMap = { 0: 'WAIT', 1: 'STRIKE', 2: 'AGGRESSIVE_STRIKE', 3: 'PAUSE' };
    }
    
    async loadModel(modelPath = '/models/trained-policy.onnx') {
        this.session = await ort.InferenceSession.create(modelPath, {
            executionProviders: ['wasm', 'cpu']
        });
        console.log('✅ Ghost Unit RL model loaded');
    }
    
    async predict(marketFeatures) {
        const inputTensor = new ort.Tensor('float32', marketFeatures, [1, 31]);
        const results = await this.session.run({ 'market_state': inputTensor });
        const actionProbs = results['action_probs'].data;
        const actionIndex = actionProbs.indexOf(Math.max(...actionProbs));
        return {
            action: this.actionMap[actionIndex],
            confidence: actionProbs[actionIndex],
            timestamp: Date.now()
        };
    }
}

class RealTimeRLMonitor {
    constructor(wsUrl, inferenceEngine) {
        this.ws = new WebSocket(wsUrl);
        this.inference = inferenceEngine;
        this.ws.onmessage = async (event) => {
            const decision = await this.inference.predict(JSON.parse(event.data));
            if (decision.action === 'STRIKE' && decision.confidence > 0.75) {
                await this.executeStrike(decision);
            }
        };
    }
    
    async executeStrike(decision) {
        const response = await fetch('/api/v1/strike/execute', {
            method: 'POST',
            body: JSON.stringify(decision)
        });
        return response.json();
    }
}

export { GhostUnitInference, RealTimeRLMonitor };

5️⃣ Reward Function

📄 rl-models/training/reward_function.py

rl-models/training/reward_function.py

import numpy as np

class RewardFunction:
    def calculate(self, action: int, profit: float, market_state: np.ndarray) -> float:
        # Financial profit (50% weight)
        if profit > 100:
            profit_reward = 10.0
        elif profit > 0:
            profit_reward = profit / 10
        else:
            profit_reward = max(-20, profit / 5)
        
        # Timing accuracy (20% weight)
        momentum = market_state[0] - market_state[5] if len(market_state) > 5 else 0
        timing_reward = 2.0 if momentum > 0.05 else (-1.0 if momentum = 10 else 0.5
        risk_reward = -2.0 if volatility > 0.1 else (1.0 if volatility 

6️⃣ Model Versioning on IPFS

📄 rl-models/ipfs_storage/model_versioning.py

rl-models/ipfs_storage/model_versioning.py

import ipfshttpclient
import hashlib
from datetime import datetime

class ImmutableModelRegistry:
    """Store every trained model version on IPFS"""
    
    def __init__(self, ipfs_gateway='/ip4/127.0.0.1/tcp/5001/http'):
        self.client = ipfshttpclient.connect(ipfs_gateway)
        
    def upload_model(self, model_path: str, metadata: dict) -> str:
        model_cid = self.client.add(model_path)['Hash']
        with open(model_path, 'rb') as f:
            sha256 = hashlib.sha256(f.read()).hexdigest()
        
        manifest = {
            'model_cid': model_cid,
            'version': metadata.get('version', '1.0.0'),
            'timestamp': datetime.utcnow().isoformat(),
            'sha256': sha256
        }
        manifest_cid = self.client.add_json(manifest)['Hash']
        print(f"✅ Model CID: {model_cid}")
        print(f"📋 Manifest CID: {manifest_cid}")
        return model_cid
    
    def load_model_version(self, model_cid: str, output_path: str):
        self.client.get(model_cid, output_path)
        return output_path

📊 Quick Reference: RL Model Actions

Action Code	Action Name	Description	Risk Level
0	WAIT	Do nothing, continue observing	🟢 Low
1	STRIKE	Execute a health trend strike	🟡 Medium
2	AGGRESSIVE_STRIKE	Double down, higher risk/reward	🔴 High
3	PAUSE	Emergency cooldown	⚫ Critical

🧠 Ghost Unit RL Engine · Immutable Model Registry · ONNX Production Ready