Summary of Parkour AI Development in Minecraft

Introduction

• Received mixed feedback on video about AI in Minecraft.
• Clarification: AI in context means human-replicating behavior, but not true AI.

Objective

• Create a Parkour Master AI that learns to navigate parkour courses.
• AI begins at a set spawning location and must find its way to the end of the course.

Initial Setup

• Built a dojo for the AI.
• Introduced a start button to begin the AI learning process.
• Summon an armor stand to represent the AI's body.

Movement Mechanics

• Use motion commands to simulate player movements.
• Establish a scoreboard to track jump heights.
• Experiment with motion values to achieve realistic jumps.

AI Learning Process

• AI needs to navigate a circular area with various degrees for jumps.
• Use unit circle principles to determine motion for the jumps.
• Establish multiple command blocks for each degree to test jump capabilities.

Reinforcement Learning

• Implemented reinforcement learning to evaluate armor stands' jump success.
• Identify which armor stand is closest to the center of the target landing area to determine the best jump.
• Created a fitness function to track and reward successful movements.

Memory System

• Developed a self-building memory system for the AI.
• Created command blocks to store successful jumps and movements.
• AI places invisible markers to remember paths taken.

Improvements Made

• Addressed issues of AI getting stuck in loops by implementing memory clearance after unsuccessful jumps.
• Created a system to handle different jump strengths and adapt to various obstacles.

Final Testing

• Tested the AI on a complex parkour course and a maze.
• Enabled the AI to recognize dead ends and backtrack effectively.
• Adjusted AI speed using new game commands for rapid learning.

Conclusion

• Successfully built an AI that can learn and navigate parkour courses.
• Open invitation for viewers to try the map.
• Call to action for subscribing to the channel.