AI Learns To Play Golf

In this video, the host introduces an AI designed to play golf, highlighting its unique approach to mastering the sport. The AI, named 'poo', utilizes four separate neural networks to handle different aspects of golf: driving, irons, chipping, and putting. Each network is trained using a reinforcement learning model with a tailored reward system to optimize performance in its respective area. The driving AI is particularly successful, achieving shots of around 210 yards despite an unconventional swing technique. The iron AI adapts to ground-level shots, while the chipping and putting AIs focus on accuracy and control, with the latter also learning to handle uneven terrain. Despite some humorous failures, the golf AI demonstrates promising capabilities, with plans to test its skills on actual golf courses in a future video.

Takeaways

• 🤖 The AI is designed to play golf, using a neural network to control its movements.
• 🏌️‍♂️ The AI's golf swing is created by splitting it into four separate neural networks for driving, irons, chipping, and putting.
• 📐 A reward system is implemented for reinforcement learning, using the cross product to determine the swing plane and direction.
• 🚀 The driving AI is trained to hit golf balls about 210 yards with a slight pull that can be adjusted for.
• 🦿 The AI's unique swing involves shifting weight and attempting a 'Superman' style move, resulting in a face plant.
• 🏋️‍♂️ For iron shots, the AI's neural network is tweaked to maintain club height close to the ground for precision.
• 🌐 The wedge AI is trained on randomized surfaces to handle uneven terrain, improving its versatility.
• 🎯 Both wedge and putting AIs focus on accuracy and control rather than speed, with adjustments to the reward system.
• 🤔 The putting AI has some issues, with a less consistent technique compared to the wedge AI.
• 📉 The AI's performance in putting could be improved by following a set of rules to ensure proper technique.
• 🌟 The golf AI is now complete with four neural networks for different types of shots, ready to be tested on a real golf course.

Q & A

What is the main focus of the video?

The main focus of the video is to demonstrate and discuss the development of an artificial intelligence (AI) system designed to play golf. The AI is trained to handle different types of golf shots using separate neural networks for driving, irons, chipping, and putting.

How many neural networks does the AI have for playing golf?

The AI has four separate neural networks, each specialized for a different aspect of golf: one for driving, one for irons, one for chipping, and one for putting.

What is the 'cross product' operation used for in the context of the AI's golf swing?

The cross product operation is used to determine the swing plane, which is the axis around which the club should be tilted to hit a perfect golf shot. It is derived from two vectors: one from the shoulder line to the club face and another from the shoulder line to the right shoulder.

How does the AI learn to swing the golf club?

The AI learns to swing the golf club through a reinforcement learning process that uses a reward system. The system rewards the AI based on how closely the club's velocity matches the target direction of the swing and punishes deviations from the swing plane.

What is the purpose of increasing the size of the golf ball in the simulation?

The size of the golf ball is increased in the simulation to address limitations of the physics engine, such as small objects phasing through solid surfaces, and to better simulate the interaction between the club and the ground during golf shots.

How does the AI's performance in driving shots compare to human golfers?

The AI's driving AI is capable of hitting golf balls roughly 210 yards, which is a competitive distance. However, there is a slight pull in its shots, which can be compensated for by aiming slightly to the right.

What adjustments are made to the AI's iron swing compared to the driver swing?

For the iron swing, the AI's reward system is tweaked to emphasize keeping the club flush with the ground. The backswing is also shortened to reduce the overall swinging speed, aiming for a more precise swing.

How does the AI handle uneven terrain during its training for the wedge and putting AI?

During training, the AI is exposed to random platforms to simulate uneven terrain. This helps the wedge AI become more resilient and adaptable to different surfaces it may encounter on actual golf courses.

What is the primary focus of the putting AI's training?

The primary focus of the putting AI's training is on accuracy and control rather than speed. The reward system is adjusted to reward how close the swing gets to the target speed, and the backswing is greatly reduced to encourage consistency.

What are the unique challenges faced by the AI when learning to play golf?

The unique challenges faced by the AI when learning to play golf include simulating realistic interactions between the club and the ground, handling different swing types for various clubs, and adapting to uneven terrain for short game shots.

What is the next step after training the AI for different types of golf shots?

The next step after training the AI for different types of golf shots is to test its performance in a real game by playing it through some golf courses.

Outlines

00:00

🤖 Introduction to AI Golf Training

The video begins by discussing the channel's collection of sporting AI and introduces a new challenge: playing golf with an AI. The trusted algorithm 'poo' is making its fourth appearance, and a red ragdoll named 'block house' is also featured. The ragdoll is equipped with a neural network to control its movements, specifically to create a golf swing. The video addresses the limitations of the physics engine used and how they plan to overcome these by increasing the size of the golf ball to ensure realistic behavior. The AI is divided into four separate neural networks, each specializing in a different aspect of golf: driving, irons, chipping, and putting. A detailed explanation of the reward system for the driving AI is provided, which includes using the cross product to determine the swing plane and the direction of the club for a perfect shot. The training results show the AI developing a unique golf swing, with some room for improvement.

05:00

🚀 Iron Shots and Short Game Training

The video moves on to training the AI for iron shots, which differ from driver swings primarily due to the stance height. To adapt, the reward system is tweaked to emphasize keeping the club close to the ground level. The backswing is shortened to reduce the swing speed, aiming for precision rather than power. The results show the AI performing consistent iron shots despite a less-than-perfect club path post-impact. The focus then shifts to the short game, emphasizing accuracy and control over distance. Additional inputs are introduced to the neural networks for the wedge and putting AI to control the swing strength. The wedge AI is trained on uneven terrain to enhance its adaptability, while the putting AI focuses on consistency, with reduced backswing. The video ends with a teaser for the next video, where the AI's performance will be tested on actual golf courses.

10:02

🏌️‍♂️ Wedge and Putting AI Performance

The wedge AI is tested and, despite falling over, shows promise with a stance and swing similar to human golfers. However, the AI's attempt to kick the ball results in failure, highlighting the need for further training on randomized surfaces. The putting AI, on the other hand, does not perform as expected, leading to a humorous set of rules for successful putting that include keeping the leg straight, adjusting the swing speed, following through stiffly, hitting the ball at a 45° angle, and conserving energy by lying down. The video concludes with the golf AI being complete with four neural networks for different types of golf shots, and a note of thanks to a supporter, with a hint of self-deprecation regarding the time taken to produce the video.

Keywords

AI

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the video, AI is used to learn and perform the complex task of playing golf, demonstrating how it can be trained through reinforcement learning to execute different types of golf shots effectively.

Neural Networks

A neural network is a series of algorithms that an AI uses to recognize patterns. They are inspired by the human brain and are designed to process complex data. In the context of the video, the AI uses separate neural networks for different aspects of golf, such as driving, irons, chipping, and putting, to learn and improve its performance.

Reinforcement Learning

Reinforcement learning is a type of machine learning where an agent learns to behave in an environment by performing actions and discovering errors. The agent is rewarded for right actions, in this case, the AI is rewarded for making accurate and effective golf swings. This learning process is central to how the AI in the video improves its golfing skills.

Swing Plane

The swing plane in golf is the path that the clubhead takes during a swing. It is critical for a golfer to maintain the correct swing plane to hit the ball accurately and with power. The AI in the video uses the concept of the swing plane to determine the axis around which it needs to tilt to perform a perfect golf shot.

Dot Product

The dot product is a mathematical operation that takes two vectors and returns a single number. In the video, the dot product is used to calculate a reward for the AI by comparing the velocity of the club to the target direction, which helps the AI to learn the correct direction for swinging the golf club.

Cross Product

The cross product is another mathematical operation between two vectors that results in a third vector that is perpendicular to the first two. In the context of the video, the cross product is used to determine the swing plane, which is essential for the AI to learn the correct golf swing technique.

Physics Engines

A physics engine is a software component of a game or simulation that is responsible for simulating the physics of the environment, such as gravity, collisions, and motion. In the video, the limitations of the physics engine are mentioned as a challenge when simulating how the golf club interacts with the ground.

Chipping

Chipping in golf is a technique used to hit the ball with a short swing, typically from close to the green. It requires precision and control rather than power. The video discusses how the AI is trained to chip by adjusting its neural network to focus on accuracy and control.

Putting

Putting is the golf stroke used on the green to play the ball into the hole. It requires a delicate touch and a good understanding of the green's slope and speed. The video describes how the putting AI is trained to focus on consistency and accuracy, with adjustments made to its neural network and reward system.

Randomized Surfaces

In the context of the video, randomized surfaces refer to the varying terrain conditions that the AI is trained on, such as flat ground, bunkers, or uneven terrain. This training helps the AI to adapt and perform well on different types of surfaces it might encounter on a golf course.

Driver

A driver, also known as a 1-wood, is the longest club in a golfer's bag and is typically used for hitting the ball over the greatest distance, usually from the tee at the start of a hole. The video explains how the AI is trained to use the driver effectively by emphasizing speed and power in its neural network.

Highlights

AI is being trained to play golf using a neural network to create a golf swing.

The AI's algorithm, named 'poo,' makes its fourth appearance on the channel.

A red ragdoll named 'block house' is used for the experiment, equipped with a neural network.

The physics engine used has limitations, such as small objects phasing through solid surfaces.

To address limitations, the size of the golf ball is increased for simulation purposes.

The AI is divided into four separate neural networks for driving, irons, chipping, and putting.

A reward system is implemented for reinforcement learning in each of the four AI networks.

The direction of the perfect golf swing is calculated using the cross product of two vectors.

The club's velocity is compared to the target direction to determine the reward in the driving AI.

The AI is encouraged to swing as hard as possible for the driver, with an emphasis on speed.

The training is successful, with the driving AI achieving shots of roughly 210 yards.

The AI developed a unique swing technique, including a shift of weight onto its right foot and a parallel leg stance at impact.

For iron shots, the AI's reward system is adjusted to keep the club flush with the ground.

The iron AI training is successful, with a consistent swing but a tendency for the club to drag along the ground.

The wedge and putting AIs are designed with an extra input to control swing strength.

The wedge AI is trained on randomized surfaces to improve resilience on uneven terrain.

The putting AI focuses on consistency rather than speed, with a reduced back swing.

The wedge AI demonstrates versatility in hitting from various surfaces, despite falling over.

The putting AI struggles with adherence to traditional golf putting principles.

The golf AI is now complete, with four neural networks for different types of golf shots.

The AI's performance in a real game will be tested in an upcoming video.