ReinforceMC
🟠 Updated over a year ago⚠️ Install Order · 2 steps
Install in this order, then this mod last — most mods fail to load without their framework and dependencies in place.
🖼️ Gallery (7 images)
🔍 Analysis
- 👍 0.6% endorsement rate
- 🏆 #260 of 400 forge mods in this game by downloads
📋 About This Mod
> *ReinforceMC is an original project started by topandrey14 / topandrey15 in May 2025. The name "ReinforceMC" is associated with this project only. Any unauthorized use of the name in commercial or misleading projects is strictly prohibited.* # 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 ✨ ### 🤖 DeepSeek + Baritone integration released! - Message DeepSeek in your own Telegram bot, and it will perform actions in Minecraft using Baritone! ### 🎯 Custom Reinforcement learning 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. --- **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.
> ReinforceMC is an original project started by topandrey14 / topandrey15 in May 2025. The name "ReinforceMC" is associated with this project only. Any unauthorized use of the name in commercial or misleading projects is strictly prohibited.
# 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 ✨ ### 🤖 DeepSeek + Baritone integration released! - Message DeepSeek in your own Telegram bot, and it will perform actions in Minecraft using Baritone! ### 🎯 Custom Reinforcement learning 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.
📊 Mod Details
- Game
- Minecraft Mods
- Author
- topandrey15
- Version
- 2.0
- Downloads
- 819
- Endorsements
- 5
- Category
- forge
- Created
- 6/19/2025
- Updated
- 8/7/2025
- Game Versions
- 1.16.5
- Tags
- game-mechanics, magic, utility
🔧 How to Install Minecraft Mods Mods
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❓ Frequently Asked Questions
Which Minecraft Mods versions does ReinforceMC support?
The author lists 1.16.5. Other versions may work but are untested by the author.
What is the latest version of ReinforceMC?
Version 2.0, published 2025-08-07 with 819 downloads recorded at the source.
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