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BlockMarket: Multi-Agent Reinforcement Learning Trading Arena, An innovative reinforcement learning platform that trains intelligent trading agents in a spatially-aware bartering economy.

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BlockMarket: Multi-Agent Reinforcement Learning Trading Arena

Track 3: RL Agent Arena Submission (Qualcomm Hackathon: Honorable Mention ("Cutest" Presentation)

minecraft picture start screen picture flask dashboard picture

An innovative reinforcement learning platform that trains intelligent trading agents in a spatially-aware bartering economy, optimized for Qualcomm Snapdragon X Elite NPU acceleration and beautifully visualized through Minecraft integration.

BlockMarket is a cutting-edge reinforcement learning environment where neural network agents learn how to maximize the amount of trades in a simulated trading floor (Kudos to the early 2000s floor traders in places like the New York Stock Exhcange (NYSE)). Our platform leverages the power of Qualcomm Snapdragon's dedicated NPU and powerful CPU to run complex neural networks locally, ensuring real-time decision-making with complete privacy preservation.

Under each folder, there is a more in-depth README for each component.

Team Members

System Architecture Overview

graph TB
    subgraph "Qualcomm Snapdragon Laptop"
        subgraph "NPU (45 TOPS)"
            NN[Neural Networks<br/>PyTorch Models]
            TM[Trading Matrix<br/>Optimizer]
        end

        subgraph "RL Monorepo (Python)"
            ENV[Trading Environment<br/>environment.py<br/>- 100+ Agents<br/>- Spatial Positions]
            AGENT[Trading Agents<br/>agent.py<br/>- Neural Networks<br/>- Trading Matrices]
            TRAIN[Training Loop<br/>training.py<br/>- Genetic Algorithm<br/>- Fitness Evaluation<br/>- LLM Summary Triggers]
            FLASK[Flask Server<br/>web_server.py<br/>Port: 5001<br/>- Real-time Metrics<br/>- WebSocket Streaming<br/>- AI Analysis Panel]
            LLM[LLM Summarizer<br/>llm_summarizer.py<br/>- Imagine SDK Integration<br/>- Market Analysis<br/>- Trading Insights]
        end

        subgraph "Express Controller Monorepo"
            subgraph "Backend (Node.js)"
                EXPRESS[Express API<br/>server.js<br/>Port: 5000<br/>- REST Endpoints<br/>- CORS Support]
                ROUTES[API Routes<br/>- /api/marketplace<br/>- /api/users<br/>- /api/transactions]
            end

            subgraph "Frontend (React + Vite)"
                REACT[React App<br/>Port: 3000<br/>- Connection Status<br/>- Trading Grid<br/>- Inventory Panel]
                HOOKS[WebSocket Hooks<br/>useSocket.js<br/>- Real-time Updates]
                ATOMS[Recoil State<br/>inventory.js<br/>- Global State Mgmt]
            end
        end

        subgraph "World Controller Monorepo (Java)"
            WS_SERVER[WebSocket Server<br/>WebSocketServer.java<br/>Port: 8080<br/>- Client Management<br/>- Message Routing]
            FLOOR_BUILDER[Trading Floor Builder<br/>TradingFloorBuilder.java<br/>- Glass Ceiling<br/>- Lighting System]
            CMD_EXEC[Command Executor<br/>CommandExecutor.java<br/>- Safe Command Execution]
        end

        subgraph "Bot Controller (Node.js)"
            MINEFLAYER[Mineflayer Controller<br/>socketRecieveNoQueue.js<br/>Port: Socket.IO<br/>- Bot Spawning<br/>- Trading Simulation]
            SIM_CLASS[Simulation Class<br/>simulationClass.js<br/>- Grid-based Bot Placement<br/>- Trade Orchestration]
            BOT_SIM[Bot Simulation<br/>sim.js<br/>- Pathfinding<br/>- Item Exchange<br/>- Visual Effects]
        end
    end

    subgraph "Minecraft Server"
        MC[Paper 1.20.6<br/>Port: 25565<br/>- 3D Visualization<br/>- Interactive World]
        PLUGIN[BM Plugin<br/>- Bukkit Integration<br/>- Event Handlers]
    end

    %% NPU Processing
    NN -.->|Accelerated<br/>Inference| AGENT
    TM -.->|Matrix<br/>Optimization| AGENT

    %% RL Internal Flow
    ENV <-->|State/<br/>Actions| AGENT
    AGENT -->|Performance<br/>Metrics| TRAIN
    TRAIN -->|New<br/>Generation| ENV
    ENV -->|Environment<br/>Data| FLASK
    TRAIN -->|Trading Data<br/>Every Half Gen| LLM
    LLM -->|AI Summaries| FLASK

    %% Express Internal Flow
    EXPRESS <-->|API Calls| ROUTES
    REACT <-->|State<br/>Updates| HOOKS
    HOOKS <-->|Global<br/>State| ATOMS

    %% Bot Controller Internal Flow
    MINEFLAYER -->|Simulation<br/>Commands| SIM_CLASS
    SIM_CLASS -->|Bot<br/>Management| BOT_SIM

    %% Socket Tunneling & Data Flow
    FLASK ===>|WebSocket<br/>Stream + AI Analysis| REACT
    REACT ===>|Commands| EXPRESS
    EXPRESS ===>|Relay| WS_SERVER
    LLM -.->|AI Analysis<br/>API Endpoints| REACT
    FLASK ===>|Socket.IO<br/>Simulation Commands| MINEFLAYER

    %% Minecraft Integration
    WS_SERVER <-->|WebSocket<br/>Protocol| PLUGIN
    PLUGIN -->|Bukkit API| MC
    FLOOR_BUILDER -->|Build<br/>Commands| PLUGIN
    CMD_EXEC -->|Execute| PLUGIN
    BOT_SIM -->|Mineflayer<br/>Bot Control| MC

    %% User Interactions
    USER[User]
    USER -->|Browser| REACT
    USER -->|Browser| FLASK
    USER -->|MC Client| MC

    %% Data Flow Types
    classDef npuStyle fill:#ff6b6b,stroke:#c92a2a,stroke-width:3px
    classDef rlStyle fill:#4ecdc4,stroke:#087f5b,stroke-width:2px
    classDef expressStyle fill:#ffd43b,stroke:#fab005,stroke-width:2px
    classDef mcStyle fill:#95d600,stroke:#5c7cfa,stroke-width:2px

    class NN,TM npuStyle
    class ENV,AGENT,TRAIN,FLASK,LLM rlStyle
    class EXPRESS,ROUTES,REACT,HOOKS,ATOMS expressStyle
    class WS_SERVER,FLOOR_BUILDER,CMD_EXEC,MC,PLUGIN mcStyle
    class MINEFLAYER,SIM_CLASS,BOT_SIM expressStyle
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How It Works

  1. Four Monorepos Architecture:

    RL Monorepo (Python):

    • environment.py: Manages 100+ trading agents with spatial positions
    • agent.py: Neural networks leveraging Snapdragon NPU for 45 TOPS inference
    • training.py: Genetic algorithm implementation for population evolution with LLM triggers
    • web_server.py: Flask server streaming real-time metrics and AI analysis via WebSocket
    • llm_summarizer.py: Imagine SDK integration for intelligent market analysis and insights

    Express Controller Monorepo (JavaScript):

    • Backend: Express.js API server with REST endpoints for marketplace data
    • Frontend: React + Vite app with Recoil state management
    • WebSocket hooks for real-time updates from Flask server
    • Responsive UI with trading grid and inventory visualization

    World Controller Monorepo (Java):

    • WebSocket server accepting commands from Express backend
    • Trading floor builder creating 3D structures in Minecraft
    • Safe command execution with configurable security

    Bot Controller Monorepo (Node.js):

    • socketRecieveNoQueue.js: Socket.IO client receiving simulation commands from RL environment
    • simulationClass.js: Manages bot grid placement and trade orchestration
    • sim.js: Core Mineflayer bot simulation with pathfinding, item exchange, and visual effects
    • Real-time bot spawning and control in Minecraft for trading visualization
    • Creative mode trading animations with particle effects and item exchanges

  2. Socket Tunneling & AI Analysis Flow:

    RL Environment → Flask WebSocket → React Frontend
                      ↓              ↑
LLM Summarizer → AI Analysis ────────┘
                      ↓
Minecraft Server ← Plugin ← WebSocket ← Express API
                      ↑
Bot Controller ← Socket.IO ← Flask RL Environment
    • Flask streams environment updates and AI analysis to React dashboard
    • LLM generates intelligent summaries every half generation (50 timesteps)
    • React displays real-time trading insights powered by Imagine SDK
    • React sends commands through Express API
    • Express relays commands to World Controller WebSocket
    • Plugin executes commands in Minecraft world
    • Bot Controller receives simulation commands from Flask via Socket.IO
    • Mineflayer bots spawn and perform trading visualizations in Minecraft

  3. NPU Acceleration:

    • PyTorch models run on Snapdragon NPU for neural network inference
    • Trading matrix optimization leverages 45 TOPS of AI performance
    • Real-time agent decision-making with minimal latency
    • Energy-efficient processing compared to CPU-only execution

  4. Integration Points:

    • Port 5001: Flask visualization dashboard
    • Port 5000: Express API server
    • Port 3000: React development server
    • Port 8080: WebSocket server for Minecraft integration
    • Port 25565: Minecraft server (Paper 1.20.6)
    • Socket.IO: Bot Controller connection to RL environment

Key Innovation Points:

  • NPU-Optimized RL Training: Neural networks are specifically optimized to leverage Snapdragon X Elite's NPU, achieving up to 45 TOPS of AI performance for real-time agent decision-making
  • AI-Powered Market Analysis: Real-time LLM summaries using Imagine SDK (Llama-3.1-8B) provide intelligent insights on trading patterns, agent performance, and market dynamics every half generation
  • Spatial Trading Dynamics: Agents exist in a 2D world where distance affects trading probability, creating realistic market dynamics
  • Minecraft Visualization: Real-world trading scenarios are brought to life through interactive Minecraft environments, making complex AI behaviors visually comprehensible
  • Automated Bot Simulation: Mineflayer-powered bots automatically spawn and perform trading activities in Minecraft, providing real-time visual feedback of AI agent interactions
  • Genetic Evolution: Population-based training with genetic algorithms ensures continuous improvement of trading strategies
  • Privacy-First Design: All AI computations run locally on the Snapdragon-powered device, ensuring complete data privacy

Overview of RL Pipeline

Summary

This is a multi-agent reinforcement learning system that simulates a trading economy where AI agents learn to trade resources optimally. It combines:

  • Genetic Algorithm: Population-based evolution
  • Neural Networks: Individual agent intelligence
  • Strategic Value Function: Multi-hop trading optimization
  • Spatial Dynamics: Distance-based trade probability

RL Setup

1. Environment Layer (environment.py)

The World Controller - Manages the entire trading ecosystem:

┌─────────────────────────────────────────┐
│           TradingEnvironment            │
├─────────────────────────────────────────┤
│ • 50 agents in 100x100 2D world        │
│ • 10 items: wood, stone, iron, gold,   │
│   food, water, coal, oil, copper,      │
│   silver                                │
│ • Market data aggregation              │
│ • Trade conflict resolution            │
│ • Genetic algorithm selection          │
└─────────────────────────────────────────┘

Key Responsibilities:

  • Agent Initialization: Creates 50 agents with random positions, inventories, and desired items
  • Market Data Collection: Aggregates all trading matrices into public market data
  • Trade Processing: Validates, resolves conflicts, and executes trades
  • Generation Management: Eliminates bottom 50% of agents, creates offspring from survivors

2. Agent Layer (agent.py)

Individual Traders - Each agent is an autonomous trading entity:

┌─────────────────────────────────────────┐
│             TradingAgent                │
├─────────────────────────────────────────┤
│ • Inventory: {item: quantity}           │
│ • Desired item: target to maximize     │
│ • Trading matrix: 10x10 exchange rates │
│ • Neural network: Policy predictor     │
│ • Strategic value function             │
└─────────────────────────────────────────┘

Agent Capabilities:

  • Matrix Updates: Neural network predicts optimal trading rates
  • Trade Selection: Chooses best trading partners and items
  • Strategic Planning: Evaluates multi-hop trading paths
  • Learning: Updates policy based on rewards

3. Neural Network Layer (network.py)

Agent Brain - Predicts optimal trading strategies:

┌─────────────────────────────────────────┐
│           TradingNetwork                │
├─────────────────────────────────────────┤
│ Input: [inventory + desired + market]   │
│ Hidden: 128-dim fully connected layers │
│ Output: 10x10 trading matrix           │
│ Constraint: Diagonal = 0 (no identity) │
└─────────────────────────────────────────┘

Core Processes

Timestep Flow (Repeated 100 times per generation)

1. Matrix Update Phase
   ├─ Each agent updates trading matrix via neural network
   └─ Environment collects all matrices → public market data

2. Trade Request Phase
   ├─ Each agent analyzes market + own state
   ├─ Selects optimal trade: (target_agent, give_item, want_item, amount)
   └─ Environment collects all requests

3. Trade Resolution Phase
   ├─ Validate requests (sufficient inventory)
   ├─ Resolve conflicts (distance-based probability)
   └─ Execute successful trades

4. Learning Phase
   ├─ Calculate rewards (inventory + strategic value)
   └─ Update neural network policies

Generation Flow (Up to 100 generations)

1. Fitness Evaluation
   ├─ Calculate each agent's performance
   └─ Rank agents by fitness score

2. Selection
   ├─ Keep top 50% of agents (25 survivors)
   └─ Eliminate bottom 50%

3. Reproduction
   ├─ Create 25 offspring from survivors
   ├─ Apply mutation (10% probability)
   └─ Reset inventories for new generation

4. Convergence Check
   ├─ Early stopping if no improvement for 20 generations
   └─ Continue or terminate based on progress

Intelligence Systems

Strategic Value Function - The Key Innovation

Instead of simple quantity maximization, agents use multi-hop strategic planning:

# Example: Agent wants gold but has wood
# Direct trade: wood → gold (rate: 0.1) = 0.1 gold per wood
# Strategic path: wood → iron → gold
#   wood → iron (rate: 0.5) = 0.5 iron per wood
#   iron → gold (rate: 0.3) = 0.15 gold per iron
#   Total: 0.5 × 0.15 = 0.075 gold per wood

# Agent chooses direct trade (0.1 > 0.075)

Strategic Features:

  • Path Finding: Breadth-first search with dynamic programming
  • Hop Penalty: Each intermediate trade reduces efficiency (0.9^hops)
  • Market Awareness: Uses real-time trading matrices from all agents
  • Opportunity Cost: Balances immediate vs. strategic gains

Learning & Evolution

Individual Learning (Neural Network)

  • Input: Current inventory + desired item + market conditions
  • Processing: 128-dimensional hidden layers
  • Output: 10×10 trading matrix (100 exchange rates)
  • Training: Gradient descent on reward signal

Population Learning (Genetic Algorithm)

  • Selection Pressure: Only top 50% survive each generation
  • Mutation: Random network weight perturbations (10% chance)
  • Diversity: Offspring inherit from different high-performing parents
  • Convergence: Early stopping prevents overfitting

Trade Mechanics

Trade Request Format: (agent_id, target_id, give_item, want_item, amount)

# Example trade request
requester = "agent_5"
target = "agent_23"
give_item = "wood"      # What I'm offering
want_item = "iron"      # What I want
amount = 5              # How much iron I want

# Validation checks:
# 1. Does agent_5 have enough wood?
# 2. Will agent_23 accept this rate?
# 3. Is the trade distance feasible?
# 4. Are the items different? (no identity trades)

Conflict Resolution

When multiple agents want to trade with the same target:

  • Distance Priority: Closer agents have higher probability
  • Probabilistic Selection: P(success) = exp(-distance/scale)
  • Fair Competition: No agent gets guaranteed trades

Emergent Behaviors

The system exhibits sophisticated emergent properties:

Market Dynamics

  • Price Discovery: Exchange rates converge to supply/demand equilibrium
  • Specialization: Agents develop expertise in specific resource chains
  • Arbitrage: Agents exploit price differences between markets

Strategic Evolution

  • Coalition Formation: Indirect cooperation through beneficial trading
  • Competitive Adaptation: Agents counter each other's strategies
  • Innovation: Novel trading patterns emerge through mutation

Economic Phenomena

  • Resource Flows: Efficient allocation of scarce resources
  • Market Cycles: Boom and bust patterns in different commodities
  • Strategic Depth: Multi-level planning and counter-planning

System Architecture

whiteboarding

┌─────────────────────────────────────────────────────────────────┐
│              Qualcomm Snapdragon Laptop                         │
│                                                                 │
│  ┌─────────────────────────────────────────────────────────┐    │
│  │         NPU (45 TOPS) - PyTorch Acceleration            │    │
│  │  • Neural Network Inference for Trading Agents          │    │
│  │  • Trading Matrix Optimization                          │    │
│  │  • Real-time Decision Making                            │    │
│  └─────────────────────────────────────────────────────────┘    │
│                                                                 │
│  ┌─────────────────────────────────────────────────────────┐    │
│  │              RL Monorepo (Python)                       │    │
│  │  • Trading Environment (100+ agents)     :5001          │    │
│  │  • Flask WebSocket Server ─────────────────┐            │    │
│  │  • Genetic Algorithm Evolution             │            │    │
│  │  • LLM Summarizer (Imagine SDK) ───────────┘            │    │
│  └────────────────────────────────────────────────────────┘    │
│                                                            │    │
│  ┌─────────────────────────────────────────────────────────┐    │
│  │        Express Controller Monorepo (JS)                 │    │
│  │  ┌───────────────────────────┐  ┌────────────────────┐  │    │
│  │  │   React Frontend :3000    │←─┤ Express API :5000  │  │    │
│  │  │   • Trading Grid UI       │  │ • REST Endpoints   │  │    │
│  │  │   • Real-time Updates     │  │ • Command Relay    │─┼───┐
│  │  │   • Inventory Panel       │  └────────────────────┘ │    │
│  │  └───────────────────────────┘                         │    │
│  └─────────────────────────────────────────────────────────┘   │
│                                                                │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │      World Controller Monorepo (Java)                   │   │
│  │  • WebSocket Server :8080 ←─────────────────────────────┘   │
│  │  • Trading Floor Builder                                │   │
│  │  • Bukkit Plugin Integration                            │   │
│  └─────────────────────────────────────────────────────────┘   │
│                                                                │
│  ┌─────────────────────────────────────────────────────────┐   │
│  │        Bot Controller Monorepo (Node.js)                │   │
│  │  • Mineflayer Bot Control (socketRecieveNoQueue.js)     │   │
│  │  • Grid-based Bot Spawning                              │   │
│  │  • Trading Visualization & Animation                    │   │
│  └───────────────┬─────────────────────────────────────────┘   │
└─────────────────────┼───────────────────────────────────────────┘
                     │
         ┌───────────▼────────────┐
         │  Minecraft Server      │
         │  Paper 1.20.6 :25565   │
         │  • 3D Trading Floors   │
         │  • Visual Simulation   │
         │  • Mineflayer Bots     │
         └────────────────────────┘

Socket Flow: RL → Flask → React → Express → WebSocket → Minecraft
Bot Control: RL → Flask → Socket.IO → Bot Controller → Minecraft Bots
AI Analysis: LLM Summarizer → Flask Dashboard (Every Half Generation)

Setup Instructions

Prerequisites

  • Hardware: Qualcomm Snapdragon X Elite powered laptop
  • Operating System: Windows 11 ARM64 or Linux ARM64
  • Software Requirements:
    • Python 3.9+ (ARM64 optimized)
    • Node.js 18+ (ARM64 build)
    • Java 17+ (ARM64 JDK)
    • Minecraft Server (Paper/Spigot 1.20+) (can run locally)

1. Clone the Repository

git clone https://github.com/yourusername/blockmarket.git
cd blockmarket

2. Set Up Python Environment (RL Components)

cd rl/

# Create ARM64-optimized virtual environment
python -m venv venv_arm64
source venv_arm64/bin/activate  # On Windows: venv_arm64\Scripts\activate

# Install NPU-optimized dependencies
pip install -r requirements.txt
pip install qai-hub  # Qualcomm AI Hub for NPU optimization

# Configure NPU backend
python configure_npu.py

3. Build BlockMarket Controller Plugin

cd ../bm-world-controller/

# Ensure ARM64 Java is being used
java -version  # Should show ARM64/aarch64

# Build the plugin
mvn clean package

# Copy to Minecraft plugins folder
cp target/bm-world-controller-0.1-SNAPSHOT.jar /path/to/minecraft/plugins/

Do follow a tutorial for locally hosting a minecraft server setup. The specific configuration we used is Paper 1.20.6, you can follow a guide to set up a minecraft server here. https://minecraft.fandom.com/wiki/Tutorials/Setting_up_a_server#Windows_instructions

Simply run a startup script off of that link for Windows. Then update the path and server url in the express service (we would set a .env if we had some more time.)

Our thing is a bit three pronged so its a little more setup, but all of it can theoretically run locally. Minecraft Servers are very single threaded CPU intensive.

5. Set Up Bot Controller (Mineflayer)

cd ../bm-mineflayer-controller/

# Install Node dependencies for Mineflayer bot control
npm install

# The entry point is socketRecieveNoQueue.js which connects via Socket.IO
# to receive simulation commands from the RL environment

6. Set Up Express Controller (Backend)

cd ../bm-express-controller/master-server/

# Install Node dependencies (ARM64 native modules)
npm install

# Configure environment
cp .env.example .env
# Edit .env to set appropriate values

7. Set Up Frontend Dashboard

cd ../frontend/

# Install dependencies
npm install

# Build for production
npm run build

8. Configure Minecraft Server

Edit plugins/bm-world-controller/config.yml:

websocket:
  enabled: true
  port: 8080
  bind-address: "127.0.0.1" # Local only for security

Running the Application

1. Start the Minecraft Server

cd /path/to/minecraft-server/
java -Xmx4G -jar paper-1.20.jar nogui

2. Launch the RL Training Environment

cd blockmarket/rl/

# Activate NPU-optimized environment
source venv_arm64/bin/activate

# Start training with NPU acceleration
python training.py --use-npu --device snapdragon

3. Start the Express Backend

cd blockmarket/bm-express-controller/master-server/
npm start

4. Start the Bot Controller

cd blockmarket/bm-mineflayer-controller/
node socketRecieveNoQueue.js

5. Launch the Visualization Dashboard

cd blockmarket/rl/
python web_server.py --port 5001

6. Access the Application

Usage Instructions

Training RL Agents

  1. Configure Training Parameters:

    cd rl/
# Edit config.yaml to adjust hyperparameters

  2. Monitor Training Progress:

    • Open web dashboard at http://localhost:5001
    • View real-time agent positions, fitness distributions, and trade networks
    • Track generation history and performance metrics

  3. Interact via Minecraft:

    • Join the Minecraft server
    • Use /createfloor <size> to create trading arenas
    • Watch agents trade in real-time within the Minecraft world

WebSocket Commands

Connect to the WebSocket server to control the environment:

// Example: Create a trading floor
{
  "type": "create_trading_floor",
  "size": 20,
  "world": "world"
}

NPU Optimization Details

Our implementation leverages Snapdragon X Elite's NPU for:

  1. Neural Network Inference: Agent decision-making runs at 45 TOPS

Some more optimizations exist that are yet to be used.

ONNX Model Conversion for NPU Acceleration

This document explains how to convert BlockMarket trading agent models from PyTorch (.pth) format to ONNX format for NPU acceleration on Qualcomm Snapdragon X Elite devices.

Overview

The ONNX conversion system enables trading agent neural networks to leverage the 45 TOPS of AI performance available on Snapdragon X Elite NPUs, providing significant acceleration for real-time inference during trading decisions.

Features

  • Automatic Model Discovery: Finds all .pth files in the models directory
  • Batch Conversion: Convert all models at once or individual models
  • Model Validation: Verifies ONNX models produce identical outputs to PyTorch versions
  • NPU Optimization Metadata: Includes optimization hints for Qualcomm NPU deployment
  • Usage Examples: Generates ready-to-use inference code

Quick Start

1. Install Dependencies
cd blockmarket/rl
pip install -r requirements.txt
2. Convert All Models
python convert_models.py
3. Convert Specific Model
python convert_models.py --model-path models/trading_agents/agent_final.pth
4. Generate NPU Usage Example
python convert_models.py --create-example

Detailed Usage

Command Line Options
python convert_models.py [OPTIONS]

Options:
  --model-path PATH     Convert specific .pth model file
  --pattern PATTERN     Glob pattern for finding .pth files (default: *.pth)
  --config PATH         Path to configuration file (default: config.yaml)
  --validate-only       Only validate existing ONNX models, do not convert
  --create-example      Create NPU inference example code
  --help               Show help message
Programmatic Usage
from onnx_conversion import ONNXConverter

# Create converter
converter = ONNXConverter()

# Convert all models
results = converter.convert_all_models()

# Convert specific model
success = converter.convert_single_model('path/to/model.pth')

# Create NPU inference example
converter.save_inference_example()

Output Structure

After conversion, the following files are created in models/trading_agents/onnx/:

models/trading_agents/onnx/
├── agent_final.onnx                    # ONNX model
├── agent_final_metadata.json          # Model metadata and NPU hints
├── agent_gen50_rank1.onnx             # Another converted model
├── agent_gen50_rank1_metadata.json    # Corresponding metadata
└── npu_inference_example.py           # Usage example code

Metadata Format

Each ONNX model includes a metadata JSON file with the following information:

{
  "model_info": {
    "onnx_path": "agent_final.onnx",
    "model_type": "TradingNetwork",
    "input_dim": 131,
    "output_dim": 25,
    "items_list": ["diamond", "gold", "apple", "emerald", "redstone"],
    "num_items": 5,
    "validation_passed": true
  },
  "agent_info": {
    "agent_id": "agent_0",
    "desired_item": "diamond",
    "training_history": 1250
  },
  "npu_optimization": {
    "target_device": "Qualcomm Snapdragon X Elite",
    "expected_tops": 45,
    "optimization_hints": [
      "Use QNN backend for inference",
      "Enable NPU execution provider",
      "Consider FP16 quantization for better performance",
      "Batch size = 1 recommended for real-time inference"
    ]
  },
  "conversion_info": {
    "opset_version": 11,
    "torch_version": "2.0.1",
    "onnx_version": "1.14.0"
  }
}

NPU Inference Example

The converter automatically generates an example showing how to use the ONNX models with NPU acceleration:

import onnxruntime as ort
import numpy as np
import json

# Configure ONNX Runtime for NPU acceleration
def create_npu_session(onnx_path: str):
    providers = [
        ('QNNExecutionProvider', {
            'backend_path': 'QnnHtp.dll',
            'profiling_level': 'basic',
            'rpc_control_latency': 1000,
            'vtcm_mb': 8,
            'htp_performance_mode': 'burst'
        }),
        'CPUExecutionProvider'  # Fallback
    ]

    session_options = ort.SessionOptions()
    session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL

    return ort.InferenceSession(onnx_path, sess_options=session_options, providers=providers)

# Run inference
session = create_npu_session("models/trading_agents/onnx/agent_final.onnx")
outputs = session.run(None, {'state_vector': input_data})

Integration with Training System

Automatic Conversion After Training

You can integrate ONNX conversion into the training pipeline by modifying the model saving process:

from onnx_conversion import ONNXConverter

# In training.py or after training completes
def save_and_convert_models(env, model_save_path, generation):
    # Save PyTorch models (existing functionality)
    save_generation_models(env, model_save_path, generation)

    # Convert to ONNX for NPU deployment
    converter = ONNXConverter()
    converter.convert_all_models()
Real-time NPU Inference

Replace PyTorch inference in the TradingAgent class with ONNX NPU inference:

class NPUTradingAgent(TradingAgent):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.onnx_session = None

    def load_onnx_model(self, onnx_path: str):
        """Load ONNX model for NPU inference."""
        self.onnx_session = create_npu_session(onnx_path)

    def update_trading_matrix(self, market_data=None):
        """NPU-accelerated trading matrix update."""
        if self.onnx_session:
            state = self.get_state_vector(market_data)
            outputs = self.onnx_session.run(None, {'state_vector': state.numpy()})
            matrix_update = outputs[0].reshape(self.num_items, self.num_items)
            # Apply update with learning rate
            learning_rate = self.config['learning']['matrix_update_rate']
            self.trading_matrix = (1 - learning_rate) * self.trading_matrix + learning_rate * matrix_update
        else:
            # Fallback to PyTorch
            super().update_trading_matrix(market_data)

Performance Expectations

On Qualcomm Snapdragon X Elite with NPU acceleration:

  • Inference Latency: ~0.1-1ms per agent decision (vs 10-50ms on CPU)
  • Throughput: Up to 1000+ agent updates per second
  • Power Efficiency: ~10x more efficient than CPU-only inference
  • Scalability: Support for 100+ concurrent trading agents in real-time

Troubleshooting

Common Issues

  1. ONNX Runtime Not Found

    pip install onnxruntime>=1.15.0

  2. QNN Provider Not Available

    • Ensure you're running on a Snapdragon X Elite device
    • Install Qualcomm AI Stack for Windows/Linux
    • Check that QnnHtp.dll is available in PATH

  3. Model Validation Failed

    • Check PyTorch and ONNX versions are compatible
    • Verify input tensor shapes match expected dimensions
    • Try reducing numerical tolerance for validation

  4. No .pth Files Found

    • Ensure you've run training and models have been saved
    • Check the model_save_path in config.yaml
    • Verify file permissions
Debug Mode

Enable verbose logging for debugging:

import logging
logging.basicConfig(level=logging.DEBUG)

converter = ONNXConverter()
converter.convert_all_models()

Advanced Configuration

Custom ONNX Export Settings
converter = ONNXConverter()

# Custom export with different opset version
converter.convert_to_onnx(
    model,
    "output.onnx",
    opset_version=13,  # Use newer opset
    dynamic_axes={'state_vector': {0: 'batch_size', 1: 'sequence_length'}}
)
Quantization for Better NPU Performance
import onnxruntime.quantization as quantization

# Quantize model for better NPU performance
quantization.quantize_dynamic(
    "model.onnx",
    "model_quantized.onnx",
    weight_type=quantization.QuantType.QUInt8
)

Contributing

When adding new neural network architectures to the BlockMarket system:

  1. Ensure your network inherits from nn.Module
  2. Add conversion support in ONNXConverter.load_pytorch_model()
  3. Update input/output dimension calculations
  4. Test ONNX conversion and validation
  5. Update metadata generation as needed

References

Testing

Run Unit Tests

cd rl/
pytest tests/ -v

Run Integration Tests

# Test WebSocket connectivity
cd bm-world-controller/
node test-websocket-client.js ws://localhost:8080

Performance Benchmarks

cd rl/
python benchmark_npu.py --iterations 1000

LLM Integration Testing

# Test LLM summarization functionality
python test_llm_integration.py

# Test endpoints manually
curl http://localhost:5001/llm_summary
curl http://localhost:5001/llm_summaries

AI-Powered Market Analysis

BlockMarket features cutting-edge LLM integration using the Imagine SDK for real-time market intelligence:

Intelligent Summarization

  • Model: Llama-3.1-8B via Imagine SDK
  • Frequency: Automated summaries every half generation (50 timesteps)
  • Analysis: Trading patterns, agent performance, market dynamics, strategic insights

Market Intelligence Features

  • Performance Metrics: Fitness distributions, success rates, trend analysis
  • Trading Patterns: Popular item exchanges, market liquidity assessment
  • Agent Insights: Top performer analysis, specialization tracking
  • Strategic Recommendations: AI-driven optimization suggestions

Technical Implementation

  • Asynchronous Processing: Non-blocking summary generation
  • API Integration: Dedicated endpoints (/llm_summary, /llm_summaries)
  • Error Handling: Graceful fallbacks for network issues
  • Environment Configuration: Automatic API key management

Additional Notes

Privacy and Security

  • All RL training runs locally on the Snapdragon device
  • LLM analysis uses secure HTTPS connection to Imagine SDK
  • No training data or model weights stored externally
  • WebSocket connections are localhost-only by default
  • Minecraft server runs in offline mode for complete isolation

Deployment

To make this application ready for release as follows we would need:

  • Windows Store (ARM64 package)
  • GitHub Releases (cross-platform)

To wrap our express (entry) service in electron, allow it to render our vite app as a webview, and containerize our minecraft server in docker. It's a bit out of scope as an RL tool, but we plan to "electronize" our application asap.

References

RL REFERENCE: cart-pole-ppo for our RL.

  1. Qualcomm Snapdragon X Elite Documentation
  2. Minecraft Plugin Development Guide

License

This project is licensed under the MIT License - see the LICENSE file for details.

Quick Start Guide

Here's the fastest way to see BlockMarket in action:

# 1. Quick setup (assuming prerequisites installed)
git clone https://github.com/yourusername/blockmarket.git
cd blockmarket
./quick-setup.sh  # Automated setup script

# 2. Launch everything
On Mac:
chmod +x start-all.sh
./start-all.sh
On Windows:
.\start-all.bat


# 3. Open browser to http://localhost:3000 to starting conditions
Note: only a 10 x 10 grid with 5 agents is supported right now

# 4. To stop all services
./stop-all.sh

What the Scripts Do

quick-setup.sh:

  • Checks for Python, Node.js, and npm
  • Creates Python virtual environment optimized for ARM64
  • Installs all RL dependencies
  • Sets up Express backend with dependencies
  • Builds React frontend for production
  • Creates configuration files
  • Detects Snapdragon/ARM64 architecture for optimization

start-all.sh:

  • Kills any existing services on required ports
  • Starts RL training environment and visualization server
  • Launches Express API backend
  • Starts React frontend development server
  • Monitors all services for crashes
  • Provides easy access to logs
  • Gracefully shuts down on Ctrl+C

stop-all.sh:

  • Stops all running BlockMarket services
  • Cleans up any orphaned processes

Powered by Qualcomm Snapdragon & Imagine SDK - Experience the future of on-device AI with 45 TOPS of performance plus intelligent LLM analysis

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BlockMarket: Multi-Agent Reinforcement Learning Trading Arena, An innovative reinforcement learning platform that trains intelligent trading agents in a spatially-aware bartering economy.

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