Minecraft-AI
Basic Information
This repository implements a reinforcement learning agent that learns to solve maze missions in the Minecraft environment. Its primary purpose is to demonstrate and provide an example of training an autonomous agent to navigate and complete maze-style tasks inside Minecraft. The project is aimed at researchers, students and developers who want a concrete RL application in a game-based simulation to observe learning behavior, experiment with navigation challenges, and reproduce or extend maze-solving experiments in the Minecraft setting.
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App Details
Features
The repository"s core feature is an RL-based agent tailored for maze navigation within Minecraft missions. It emphasizes learning-based approaches to solve spatial navigation problems and showcases agent behavior on maze tasks. The project centers on training and evaluating the agent in in-game mission scenarios and offers a self-contained example of applying reinforcement learning in a complex simulated environment. It is organized around demonstration of learned policies and their performance on maze missions.
Use Cases
The project is helpful as a concrete application of reinforcement learning in a popular game environment, allowing users to study how agents learn to navigate mazes, design reward structures, and measure performance. It can serve as a baseline or educational example for researchers and learners exploring RL in simulated worlds, and as a starting point for adapting algorithms, modifying mission difficulty, or comparing training outcomes. The repository illustrates practical challenges of applying RL to spatial tasks in Minecraft and supports experimentation and learning.
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