AI Learns To Play Golf
TLDRThe video script discusses the development and training of an AI to play golf, using a ragd doll and four separate neural networks for different aspects of the game. The AI learns through reinforcement learning, with a reward system that adjusts based on the type of shot. Despite some humorous failures, the AI becomes capable of impressive shots, showcasing adaptability and potential for real-game performance.
Takeaways
- 🤖 The video discusses the development and training of an AI system to play golf, utilizing multiple neural networks for different aspects of the game.
- 🏌️♂️ The AI is divided into four separate neural networks, each specialized for driving, irons, chipping, and putting.
- 🚀 Reinforcement learning is used to train the AI, with a reward system designed to encourage specific behaviors and correct mistakes.
- 🎯 The reward system for the driver AI focuses on the club's velocity and direction, emphasizing a fast and accurate swing.
- 🏓 For iron play, the AI is trained to maintain the club's height above the ground and adjust for a more precise swing.
- 🌲 The wedge AI is prepared to handle uneven terrain by simulating various standing conditions during training.
- 💥 The putting AI is trained with a reduced backswing and focuses on consistency rather than speed.
- 🤹♂️ Despite the AI's unconventional techniques, such as a face plant after hitting the ball, it manages to learn effective golf swings.
- 📈 The training yields mixed results, with the AI showing promise in long drives and versatility in different shot types, but struggles with putting accuracy.
- 🏌️♀️ The video concludes with plans to test the AI's performance in a real golf game, highlighting the potential and limitations of AI in sports.
Q & A
What is the main focus of the video?
-The main focus of the video is to demonstrate the development and training of an AI system to play golf, using neural networks to control different types of golf swings.
How many appearances has the 'algorithm poo' made on the channel?
-The 'algorithm poo' has made its fourth appearance on the channel.
What is the physical specification of the red ragd doll used in the video?
-The red ragd doll is 6 feet tall and weighs about 187 pounds, which is equivalent to approximately 1 and 1/2 hours of beer at the allei pal.
How does the video address the limitations of the physics engine in simulating golf?
-To address the limitations of the physics engine, the video suggests increasing the size of the golf ball to prevent small objects from phasing through solid surfaces and to better simulate the club's interaction with the ground.
How many separate neural networks are used for the AI in the video, and what are they for?
-Four separate neural networks are used for the AI. One for driving, one for irons, one for chipping, and one for putting.
What is the reward system for the driver AI, and how does it encourage proper swinging?
-The reward system for the driver AI involves using the dot product of the club's velocity and the target direction to encourage the AI to match the velocity with the desired direction. Additionally, the system measures how far the club has deviated from the swing plane and gives out a punishment that scales with the deviation to guide the club back on target.
How is the reward system for the iron AI different from that of the driver AI?
-The reward system for the iron AI is slightly tweaked from the driver AI's system. It includes measuring the height of the club from the ground and giving a reward based on how close it is to being flush. It also shortens the backswing, focusing on precision rather than speed.
What additional input is added to the neural networks for the wedge and putting AIs?
-An extra input is added to the neural networks for the wedge and putting AIs to control how hard they swing, shifting the focus from rewarding speed to rewarding how close the swing gets to the target speed.
How does the video ensure that the wedge AI can handle uneven terrain?
-During training, the video simulates uneven terrain by generating a random platform for the ragd doll to stand on for every shot attempt. This forces the wedge AI to become more adaptable and resilient to uneven conditions it may face on actual golf courses.
What is the main challenge for the putting AI compared to other types of golf AI?
-The main challenge for the putting AI is to focus on accuracy and consistency rather than speed, as putting is typically done on the flattest part of the golf course, requiring precise control over the swing.
How does the video conclude on the overall performance of the golf AI?
-The video concludes that the golf AI has been successfully trained, with each neural network learning to handle different types of golf shots. Despite some unconventional movements and a face plant, the AI demonstrates a unique and interesting swing that manages to hit the ball effectively.
Outlines
🏌️♂️ Golf AI Development and Physics Challenges
This paragraph introduces the ongoing project of developing a golf AI on the channel, emphasizing the desire to expand the collection of sporting AI. The AI, named 'poo', is set to make its fourth appearance, and the 'red ragd doll', or 'block house', will also return, equipped with a neural network to control its actions. The challenges of physics engines, particularly with small objects and club interaction, are discussed, leading to the decision to increase the ball size for more accurate simulation. The paragraph delves into the complexities of golf, highlighting the need for different swings for various clubs and the strategy to split the AI into four neural networks, each specialized for driving, irons, chipping, and putting. A detailed explanation of the reward system for the driver AI is provided, including the use of cross products and dot products to guide the club's movement and velocity. The paragraph concludes with a brief overview of the training results, noting a slight pull in the AI's shots but overall success in learning to hit golf balls approximately 210 yards.
🏌️♀️ Iron Swings and Adapting the AI
The second paragraph shifts focus to applying the same algorithm to iron swings, noting the differences between iron and driver swings, particularly the challenge of hitting from the ground. The reward system is tweaked to emphasize keeping the club flush with the ground, and the backswing is shortened for a more precise swing. The training results show that the AI maintains a consistent technique, pivoting onto its left foot and adapting to the slightly adjusted reward system. However, the AI's performance ends with a face plant, indicating room for improvement. The paragraph also discusses the next steps in training the AI for short game, emphasizing accuracy and control over distance, and introduces changes to the reward systems for the wedge and putting AI to focus on target speed and adaptability to uneven terrain.
🤖 AI Performance and Real-Game Testing
The final paragraph presents a mixed bag of results from the AI's training. The wedge AI demonstrates versatility, being able to hit from various surfaces, although the putting AI struggles with basic principles. A set of rules is humorously provided for effective putting, followed by an acknowledgment that the AI's performance is 'good enough'. The paragraph concludes with anticipation for the next video, where the golf AI will be tested on actual golf courses, hinting at the potential for a 'hole in one'. Special thanks are given to the Banana Fish C for supporting the video, and a playful nod to the time taken to produce the content.
Mindmap
Keywords
💡AI
💡Neural Networks
💡Reinforcement Learning
💡Golf Swing
💡Physics Engines
💡Swing Plane
💡Dot Product
💡Cross Product
💡Golf Clubs
💡Randomized Surfaces
💡Putt
Highlights
The development of a golf AI using neural networks to control a ragd doll, which is 6 feet tall and approximately 187 pounds.
The AI's neural network is designed to create a golf swing, with the challenge of simulating the club's interaction with the ground and small objects' behavior in the physics engine.
To address limitations in the physics engine, the ball's size is increased, and its behavior, including rolling friction, bounciness, and shot shaping, is made to mimic a real golf ball.
The AI is divided into four separate neural networks, each specialized for driving, irons, chipping, and putting, utilizing reinforcement learning techniques.
A reward system is established for each neural network, using the dot product of the club's velocity and the target direction to encourage correct swing direction.
The swing plane is introduced as the axis around which the club should be tilted to achieve a perfect golf shot.
The AI's training includes learning to swing as hard as possible for the driver to achieve the longest shot, emphasizing speed and power.
The iron AI is trained with a slightly modified reward system, focusing on keeping the club flush with the ground for a more precise swing.
The wedge and putting AIs are trained with an extra input to control the swing's hardness and focus on accuracy and control rather than distance.
The wedge AI learns to adapt to uneven terrain by simulating various surfaces during training, enhancing its resilience for real-world application.
The putting AI is trained with a reduced backswing to focus on consistency, as putting is typically done on the flattest part of the golf course.
The driving AI achieves impressive results, with the ability to hit golf balls roughly 210 yards, despite a slight pull in its shots.
The iron AI demonstrates a consistent technique, pivoting onto its left foot and maintaining a stable position until striking the ball.
The wedge AI exhibits versatility, capable of hitting balls from various surfaces such as flat ground, bunkers, trees, and even inside a clubhouse.
The putting AI, however, struggles with its technique, failing to apply proper principles of putting, resulting in a less effective performance.
The golf AI system, with its four neural networks, is set to be tested in a real game scenario in a future video, showcasing its adaptability and performance.
