ReinforceMC 🧠

A Minecraft mod that implements reinforcement learning capabilities, allowing neural networks to learn and play Minecraft through configurable reward systems and input processing.

Features ✨

🎯 Neural Network Training

• Start, stop, pause, and resume training sessions
• Real-time performance monitoring with reward tracking
• Emergency stop functionality for immediate training halt
• Model saving and loading capabilities

🏆 Configurable Reward System

• Set custom rewards for various in-game actions:
  • ⚔️ Combat: Kill players/mobs, take damage, heal
  • 🏗️ Building: Break/place blocks, craft items
  • 🚶 Exploration: Movement, sprinting, jumping, sneaking
  • 📦 Items: Pickup, drop, use, enchant items
  • 👤 Character: Gain XP, level changes, eat food

📊 Input Configuration

• 📷 Screenshot Input: Configurable resolution for visual learning
• 📍 Other Player Coordinates: Position and movement data
• 📈 Your Character Statistics: Health, hunger, XP, inventory information
• 👥 Nearby Player Tracking: Detect and track other players

📋 Reward Template System

• Save and load reward configurations
• Export templates to JSON format
• Create custom training scenarios
• Share configurations between sessions

🖥️ User Interface

• Comprehensive GUI for all configuration options
• Real-time training status and statistics
• Performance monitor with detailed metrics
• Available keys editor for input customization

Installation 🔧

📋 Requirements

• 🎮 Minecraft 1.16.5
• ⚡ Minecraft Forge 36.2.42 or compatible version
• ☕ Java 8 or higher
• 🖥️ GPU Support: NVIDIA GPU with CUDA 12.1 support (optional, falls back to CPU, but may be slower)
• 💾 Memory: At least 4GB RAM allocated to Minecraft

🚀 Setup

• The mod will automatically download required neural network libraries on first run, it may take up to 5 minutes for Minecraft to firstly load the mod. It will never happen again.

Usage 🎮

🌟 Getting Started

1. Open the ReinforceMC GUI using the configured keybind (default: check key bindings menu)
2. Configure your input settings (screenshot resolution, enabled inputs)
3. Set up reward parameters for desired behaviors
4. Configure available keys the AI can use
5. Start training and monitor progress

⌨️ Key Controls

• 🖥️ Open GUI: Configurable keybind
• 🔄 Toggle Training: Quick start/stop training
• 🛑 Emergency Stop: F1 (immediate pause/resume)

⚙️ Configuration

📥 Input Types

• 📷📍 Screenshot + Coordinates: Full visual and positional data
• 📷 Screenshot Only: Visual learning without position data
• 📍 Coordinates Only: Position-based learning without visual input

🏆 Reward Configuration

Configure positive or negative rewards for specific actions. Higher rewards encourage the behavior, while negative rewards discourage it.

⚡ Performance Considerations

• Higher screenshot resolutions increase VRAM usage and processing time
• GPU acceleration significantly improves training speed
• Monitor system resources during training

Technical Details 🔧

📚 Dependencies

• 🧠 DJL (Deep Java Library): Neural network framework
• 🔥 PyTorch: Backend neural network engine
• 🚀 CUDA Support: GPU acceleration for compatible hardware

💾 Data Storage

• Models are saved in .minecraft/mods/reinforcemc/
• Templates are stored in .minecraft/mods/reinforcemc/templates/
• Configuration files use JSON format

📊 Performance

• 💻 CPU Mode: Compatible with all systems, slower training
• 🖥️ GPU Mode: Requires CUDA-compatible GPU, faster training
• 🧠 Memory: Automatically manages memory allocation based on input configuration

Screenshots 📸

The mod includes a comprehensive GUI system with multiple configuration screens:

• 🏠 Main configuration menu
• 🏆 Reward system setup
• 📊 Input configuration
• 🎯 Training control panel
• 📈 Performance monitoring
• 📋 Template management

Compatibility 🔧

• 🎮 Minecraft Version: 1.16.5
• ⚡ Forge Version: 36.2.42+
• 💻 Operating System: Windows (primary), other platforms may work with CPU-only mode
• 💾 Memory: Minimum 4GB RAM recommended

License 📄

This project is licensed under the MIT License.

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Note: This mod requires computational resources and is intended for users familiar with at least basic machine learning concepts. Training times can vary greatly depending on hardware configuration and complexity of the learning task.