drkostas / minecraft-ai

A Reinforcement Learning agent that learns how to to solve maze missions in Minecraft.

• 📖 Pursuing PhD in Data Science & Engineering @ The University of Tennessee.

• 🎓 Conducting research on AI and Computer Vision @ the AICIP Lab.

• 💻 Currently building Masked Image Modeling models for Remote Sensing data.

🖥️ Open-Source Projects

Machine Learning	PyPi Packages
Title Stars Technologies Minecraft-AI 3D Semantic Segmentation Bert Rinehart Novels Car Accidents Pred. Hybrid Girvan Newman COVID19 Vacc. Pred. Instagram Likes Pred. RL Value Iteration Vanilla Numpy CNN Vanilla Numpy NN	Title Stars Technologies High SQL Cloud File Manager YAML Wrapper Color Logger Email Sender Benchmark Tools

Bots	Misc Projects
Title Stars Technologies Youtube Comment Bot Job Application Bot	Title Stars Technologies Spotify Button Presser Cross The Floor 2D Shooter Game Quantum Mechanics Quiz App

📈 Stats

My Github Stats

Currently Coding & Listening to:

minecraft-ai's Issues

Create first agent and train it on the first mission

Set up way to create custom missions

Dynamic Environment and Adaptive Learning Strategies

Hi,

The Minecraft AI project is a fantastic demonstration of reinforcement learning. I have a few advanced suggestions to enhance the agent's learning and adaptability:

Suggested Enhancements:

1. Dynamic Environment:

   • Adaptive Obstacles: Introduce moving obstacles and changing environmental conditions (e.g., weather effects) to challenge the agent's adaptability.
   • Randomized Maze Layouts: Generate different maze configurations for each episode to prevent the agent from memorizing the layout.

2. Advanced Reward Structure:

   • Hierarchical Rewards: Implement a multi-tiered reward system that includes intermediate checkpoints and sub-goals to guide the agent's progress.
   • Exploration Incentives: Provide rewards for exploring new areas to encourage thorough investigation of the maze.

3Adaptive Learning Strategies:

• Curriculum Learning:Start with simpler mazes and gradually increase complexity as the agent's performance improves.
• Meta-Learning Allow the agent to adapt its learning rate and strategies based on performance feedback.

4. Improved State Representations:
   • Augmented Visual Input: Include additional sensory inputs, such as depth perception or object recognition, to enhance the agent's understanding of the environment.
     -Feature Extraction: Use advanced techniques like convolutional neural networks (CNNs) to process raw pixel data more effectively.

Benefits:

• Enhanced agent robustness and generalization to new environments.
• More efficient learning through structured rewards and adaptive strategies.
• Improved performance in complex and dynamic scenarios.

Thank you for considering these suggestions to take the project to the next level!

Ruby Poddar

Set up environment with basic example

Create first mission

Try training on more (complicated) missions

Use different types of agents and improve the performance

Create visualizations with the results, finalize Readme, and create report