A.I. Learns to play Snake using Deep Q Learning

The video script details the creator's journey in developing an AI to play the game Snake using Deep Q Learning. After a three-month hiatus, the creator shares their initial attempt using a basic neural network and evolution for intelligent behavior. They then introduce Q Learning, a method to train the AI through rewards and punishments, aiming to create a 'god of snake walt.' Despite challenges such as defining the AI's vision and managing excessive inputs, the AI, named Adrian, shows improvement over 5,000 games but still falls short of perfect gameplay. The creator considers simplifying the game view for faster learning but acknowledges that it won't lead to winning the game. The video concludes with a teaser for future content and a discount offer for a problem-solving website, suggesting a fourth installment on the topic.

Takeaways

• 🎉 The creator has returned after a three-month hiatus and is working on improving their website's landing page.
• 🐍 The project involves teaching an AI to play Snake using Deep Q-Learning, aiming for an AI that can achieve perfect gameplay.
• 👀 Initially, the AI's vision was limited to eight directions, but it was later expanded to the entire screen to provide more context.
• 🚀 The AI's perception was improved by using frame stacking, allowing it to see both the current and previous positions of the snake.
• 🧠 Despite the complexity, the AI struggled with the high volume of inputs, leading to suboptimal performance even after 24 hours of learning.
• 🔍 The map size was reduced to decrease the number of inputs and improve the AI's ability to learn effectively.
• 🍎 The AI was rewarded for eating apples and punished for dying, a fundamental aspect of the Q-Learning algorithm.
• 🤖 After many games, the AI developed a strategy, but it was still inefficient and struggled when the snake grew longer.
• 📉 The creator researched other implementations of Q-Learning for Snake and found that no AI had successfully beaten the game.
• 🔧 A simplified version of the game was tried, which improved performance but still could not achieve a win.
• ⏱️ The creator committed to further development, despite the challenges, intending to create a four-part video series on the topic.

Q & A

What is the main topic of the video transcript?

The main topic of the video transcript is the process of teaching an AI to play the game Snake using Deep Q Learning.

Why did the creator decide to revisit the Snake game?

The creator decided to revisit the Snake game because it was the subject of the first two videos on his channel and he wanted to add an improved version of the game to his website.

What is Q Learning?

Q Learning is a type of machine learning algorithm used for solving certain decision problems, often in the context of Markov Decision Processes. It is a value-based algorithm that aims to learn a policy that tells an agent what action to take under what circumstances.

How does the AI learn to play Snake in the video?

The AI learns to play Snake by using a neural network and Q Learning algorithm. It is rewarded for actions that lead to food and punished for actions that result in the game ending, thus learning to navigate towards the food while avoiding obstacles.

What was the issue with the initial approach to giving the AI vision in the game?

The initial approach gave the AI vision of the entire screen, which resulted in too many inputs for the AI to process effectively. This led to the AI struggling to learn and perform well in the game.

How did the creator solve the problem of too many inputs for the AI?

The creator solved the problem by reducing the AI's field of vision to a 20 by 20 pixel square around the head of the snake, which significantly reduced the number of inputs the AI had to process.

What was the final outcome of the AI's training after playing 5000 games of Snake?

After playing 5000 games, the AI, named Adrian, showed some improvement in its strategy but still struggled with longer games and trapping itself, indicating that it was not performing optimally.

What did the creator learn from his research on other people's attempts to use Q Learning for Snake?

The creator learned that no one had been able to use Q Learning to create an AI that could beat the game of Snake. Many solutions simplified the game to speed up learning, but this limited the AI's ability to complete the game successfully.

What is the creator's plan for future videos?

The creator plans to continue working on the Snake AI, aiming to improve its performance. He also mentions that he is almost finished with the next video on the topic and has other new content planned for the future.

What is the significance of the 'burning dogs' website mentioned in the transcript?

The 'burning dogs' website is a problem-solving platform that offers interactive courses in computer science and programming. The creator recommends it as a resource for those interested in learning more about reinforcement learning algorithms and other computer science concepts.

What is the creator's opinion on the AI's performance after training it for a large number of games?

The creator acknowledges that the AI's performance is not perfect and that it has room for improvement. However, he also expresses frustration with the limitations of the current approach and considers further research and experimentation.

Outlines

00:00

😀 Return and Introduction to the Snake Game Project

The speaker returns after a three-month hiatus, catching up on personal activities, including watching Brooklyn Nine-Nine and coding. The main focus is on creating an improved version of a snake game for their website. They discuss the initial plan to add an AI-driven evolution simulator, the need to fix the website's appearance, and the decision to replace the landing page. The speaker outlines their thought process leading to the creation of a 'god of snake walt' and the intention to use Q-learning for the AI's intelligence. They also briefly explain Q-learning through a sock puppet analogy and discuss the challenges of implementing it with too many inputs, leading to the idea of frame stacking to give the AI memory.

05:01

🧠 Q-Learning and Reducing Inputs for Efficient AI Training

The speaker continues to discuss the development of the AI for the snake game, highlighting the initial issues with using a high number of inputs which led to slow learning progress. They explore solutions such as shrinking the game map and adjusting the AI's field of vision to improve efficiency. The speaker explains the concept of Q-learning in the context of rewarding and punishing the AI for its actions, leading to learned behavior. They detail the AI's training process, starting with random movements and gradually learning to avoid walls and seek apples. Despite thousands of games played for training, the AI's performance is still suboptimal, leading to the consideration of additional training sessions.

10:03

🔍 Research and Future Directions for the Snake Game AI

After acknowledging the limitations in their approach, the speaker decides to research how others have implemented Q-learning for snake games. They discover that most solutions simplify the game significantly, which expedites learning but limits the AI's ability to win. The speaker then tests a simplified approach themselves, finding it to be an improvement but still insufficient for winning. They conclude that while their AI, named Adrian, may not be perfect, they remain committed to the goal of creating a superior AI for the snake game. The speaker also promotes a resource for learning about computer science and algorithms, Burnt Dog's courses, and provides a discount link for their audience. They end with an update on their progress on the next video and a promise to release it soon.

Keywords

Deep Q Learning

Deep Q Learning is a machine learning technique that combines deep neural networks with Q Learning, a type of reinforcement learning. It is used to train an AI to make decisions in a way that maximizes a cumulative reward. In the video, the creator discusses using Deep Q Learning to train an AI to play the game Snake, aiming to make it intelligent enough to achieve a high score.

Snake Game

Snake is a classic video game where the player controls a line which grows in length, with the goal of eating apples that appear on the screen. The game ends if the snake's head collides with its own body or the screen edges. In the video, the focus is on creating an AI that can play Snake effectively using Deep Q Learning.

Reinforcement Learning

Reinforcement Learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize a reward. It's foundational in training AIs to perform tasks. The video script describes using reinforcement learning to train the AI for the Snake game, where the AI learns from its experiences, such as getting food and avoiding collisions.

Neural Network

A Neural Network is a computational model inspired by the human brain that is used in machine learning to recognize patterns and make predictions. In the context of the video, a neural network is used as part of the Q Learning algorithm to enable the AI to process visual information from the game and make decisions accordingly.

Q Learning

Q Learning is a value-based reinforcement learning algorithm that aims to learn a policy which tells the agent what action to take under what circumstances. The video uses an abridged version of a sock puppet show to explain Q Learning, emphasizing how the AI learns from experiences of being rewarded or punished for its actions in the game.

Frame Stacking

Frame Stacking is a technique used in the video to give the AI a sense of memory by feeding it two frames of the screen: the previous frame and the current frame. This helps the AI to understand the movement and change in the game state, which is crucial for making informed decisions in the game of Snake.

AI Vision

AI Vision in the context of this video refers to the AI's ability to perceive the game environment, similar to how a human player would. The script discusses changing the AI's vision from seeing only eight directions to having a full view of the screen, and then to a more focused 20 by 20 vision square around the snake's head to simplify the input data for the neural network.

Evolutionary Algorithms

Evolutionary Algorithms are optimization techniques inspired by the process of natural evolution, such as reproduction, mutation, recombination, and selection. The video mentions using a basic neural network with a sprinkle of evolution in a previous attempt to create intelligent behavior in the AI, although the focus has shifted to Q Learning for this video.

Coding

Coding is the process of writing computer programs to solve a problem or create a software application. The video script describes the process of coding the AI to play Snake, which involves implementing the Deep Q Learning algorithm and adjusting the AI's vision to improve its gameplay.

Machine Learning

Machine Learning is a field of artificial intelligence that involves the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions, instead relying on patterns and inference. The video is centered around using machine learning to train an AI to play Snake at an advanced level.

Bottleneck

In the context of the video, a Bottleneck refers to a limiting factor or issue that prevents the AI from improving its performance. The creator discusses several bottlenecks, such as the high number of inputs the AI has to process, which hinders its ability to learn efficiently and play the game effectively.

Highlights

The video discusses the use of Deep Q Learning to teach an AI to play the game Snake.

The creator has been away for three months and shares their progress since then.

They finished an AI walk simulator and encountered issues with the website's appearance.

The landing page of the website needs an update to reflect the current subscriber count.

The idea is to create a perfect and beautiful version of Snake for the website.

The Snake game is recreated from scratch to serve as a basis for the AI learning process.

Q Learning, a type of machine learning algorithm, is chosen for the AI to learn the game.

A simplified sock puppet show is used to explain the concept of Q Learning.

The AI's vision is initially set to cover the entire screen, which proves to be too much information.

Frame stacking is introduced to give the AI a sense of the snake's previous position.

The AI struggles with too many inputs, leading to suboptimal learning outcomes.

The solution is to shrink the map and focus the AI's vision around the snake's head.

After 1,000 games, the AI starts to show basic understanding and avoidance of walls.

Further training up to 2,000 games improves the AI's strategy for finding food.

Even after 5,000 games, the AI's performance is inconsistent and not optimal.

The creator considers additional changes to the AI, such as providing direction to the apple.

Research into other implementations of Q Learning for Snake reveals similar struggles.

The creator decides to continue the series with another video focusing on Snake.

A recommendation is made for those interested in learning more about reinforcement learning to check out 'Burning Dogs'.

The video concludes with an update on upcoming projects and a promise for more consistent content releases.