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

Code Bullet
12 Jul 201915:14

TLDRIn this video, the creator discusses his return to content creation after a three-month hiatus and shares his journey in developing an AI to play the classic game Snake using Deep Q Learning. He provides a brief explanation of Q Learning through a sock puppet analogy and details the challenges faced in training the AI to perceive the game environment effectively. The video showcases the AI's progress through multiple stages of training, highlighting its initial random movements, gradual learning to avoid walls, and eventual development of a strategy to locate and eat apples. Despite the AI's improvement, it still struggles with longer games. The creator also compares his AI's performance with other implementations, noting the limitations of the Q Learning approach for this task. He concludes by teasing upcoming content, including a continuation of the Snake series and a new project, while also promoting a problem-solving website for learning computer science.

Takeaways

  • 🎉 The creator is back after a three-month hiatus and has been working on various projects including a simulator and website improvements.
  • 🐍 The focus of the video is on developing an AI to play the game Snake using Deep Q Learning, aiming to create a 'god of snake waltz'.
  • 👾 The initial attempt at creating the snake game was straightforward, but the challenge lies in applying Q Learning to achieve intelligent behavior.
  • 📈 Q Learning is introduced as the algorithm of choice, with a simplified explanation through a sock puppet show analogy.
  • 👀 The AI's vision was a challenge; initially, it could see in eight directions, but later the entire screen of pixels was used to give it more information.
  • 💡 A solution to the AI's lack of memory was proposed by using frame stacking, allowing the AI to see both the previous and current positions of the snake.
  • 🚫 The initial approach of feeding the AI too many inputs led to suboptimal learning outcomes, highlighting the need to reduce the complexity.
  • 🔍 Reducing the map size or the visible area around the snake's head were considered to simplify the input for the AI.
  • 🍎 The AI was rewarded for getting food and punished for dying, which helped it learn the objectives of the game over time.
  • 📉 Despite extensive training, the AI named Adrianne did not significantly improve, leading to the consideration of alternative strategies or research.
  • 🔄 The creator researched other implementations of Q Learning for Snake and found that simplification of the game view can speed up learning but limits the AI's performance.
  • ⏭ The creator plans to continue working on the AI with potential new strategies and will release more videos on the topic.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is about 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 training an AI to make decisions. It involves rewarding and punishing the AI based on its actions to help it learn the best strategies for achieving a goal.

  • How does the AI perceive the game environment?

    -Initially, the AI perceives the game environment through the entire screen of pixels, similar to how a human would see it. Later, to simplify the problem, the AI's view is limited to a 20 by 20 vision square around the head of the snake.

  • What challenges did the creator face when implementing Q Learning for the Snake game?

    -The creator faced challenges such as managing the vast amount of inputs when using the entire screen pixels, identifying the snake's head, and finding an efficient strategy for the AI to navigate and find food in the game.

  • How many games did the AI play before showing significant improvement?

    -The AI played a total of 5,000 games before showing some improvement in its strategy for playing the Snake game.

  • What was the final outcome of the AI's performance after training?

    -Even after extensive training, the AI named Adrianne did not perform optimally and still struggled with certain aspects of the game, leading the creator to accept it as a learning experience.

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

    -The creator learned that many solutions simplified the game significantly for the AI, which allowed for faster learning but couldn't achieve a winning strategy. He also found that no one had been able to create an AI that could beat the game using Q Learning.

  • What is the name of the problem-solving website and app that the creator recommends for learning about reinforcement learning algorithms?

    -The creator recommends 'Burning Dog' as a problem-solving website and app for learning about reinforcement learning algorithms.

  • What is the creator's plan for future videos?

    -The creator plans to continue working on the Snake game and improve the AI in a subsequent video. He also mentions working on a 'Day I Loves to Walk' simulator and hints at other new projects without specifying details.

  • How does the creator describe the AI's initial strategy for finding food in the Snake game?

    -The creator describes the AI's initial strategy as inefficient, with the AI moving around randomly until it sees the apple, which doesn't seem to be the most effective way to search for food.

Outlines

00:00

😀 Return and Recap of Recent Activities

The speaker returns after a three-month hiatus, catching up on what they've been doing. They mention completing a simulator project and some coding. The plan was to add the simulator to their website, but it needs improvements. They also discuss changing their landing page, which is outdated with a 300,000 subscriber count. The speaker plans to create a new feature for their website, a sophisticated snake game, and outlines the first steps in creating the game, including making the snake and adding an apple.

05:01

🤖 Implementing Q-Learning for Snake Game AI

The speaker decides to use Q-learning for the snake game's AI, providing a simplified explanation through a sock puppet analogy. They discuss the AI's vision, initially giving it the entire screen's pixels, which leads to problems with identifying the snake's head and body. To solve this, they implement frame stacking, allowing the AI to see both the previous and current positions. However, this results in too many inputs for the AI to process efficiently. After considering shrinking the game map, they opt to limit the AI's vision to a 20x20 square around the snake's head, reducing the input load. The speaker explains the Q-learning process, where the AI learns from experiences of being rewarded for food and punished for dying, and shares the AI's progress after 1,000 and 2,000 games, noting the AI's inefficient search strategy and difficulty with longer snake bodies.

10:03

🕵️‍♂️ Research and Future Plans for Snake Game AI

After extensive training, the speaker acknowledges that their AI, named Adrian, has not improved as much as desired. They consider making changes to the AI, such as providing direction to the apple or expanding the neural network. Instead, they conduct research on how others have approached snake game AI using Q-learning. They find that most solutions simplify the game significantly, which helps with faster learning but isn't sufficient to win the game. Despite the challenges, the speaker remains determined to create a superior AI and announces plans for additional videos on the topic. They also promote a problem-solving website, Brilliant.org, and its courses on computer science and algorithms, offering a discount for their viewers. The speaker concludes by apologizing for the long wait for new content and shares their progress on upcoming videos.

Mindmap

Keywords

💡Deep Q Learning

Deep Q Learning is an advanced machine learning algorithm used for optimizing decision-making processes in environments with a defined set of actions, states, and rewards. In the video, the creator applies Deep Q Learning to train an AI to play the game Snake, aiming for the AI to achieve perfect gameplay by learning from its actions and their consequences. The algorithm's effectiveness is tested over thousands of games, adjusting strategies based on the rewards (eating apples) and punishments (hitting walls or the snake's body).

💡AI

AI, or artificial intelligence, refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the context of the video, the AI is designed to learn and perfect the game of Snake using reinforcement learning techniques. The AI's development, from initial random movements to a strategic approach to the game, illustrates the learning process and capabilities of AI systems.

💡Neural network

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In the video, neural networks are employed to enable the AI to make decisions based on the game environment. This includes perceiving the layout of the game and predicting the best moves to avoid obstacles and capture targets.

💡Frame stacking

Frame stacking is a technique used in video processing where multiple frames are layered over each other to provide a temporal context. This helps an AI understand movement and changes over time. In the video, frame stacking is used to help the AI perceive the direction and movement of the snake in the game, thereby enhancing its decision-making process by giving it a 'memory' of previous states.

💡Reinforcement learning

Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. The video extensively discusses applying reinforcement learning to teach an AI to play Snake, where the AI learns optimal gameplay strategies through trial and error and rewards-based feedback.

💡Training

Training in the context of AI development refers to the process of teaching a machine learning model to make accurate predictions or decisions by feeding it data and allowing it to adjust its strategies. The video describes how the AI for Snake was trained over thousands of games to refine its ability to avoid walls and seek apples, illustrating how training improves AI performance over time.

💡Inputs

In machine learning, inputs are the data received by an algorithm to process and from which to learn. In the video, the AI's inputs initially include the entire game screen (as pixels), but this is later refined to reduce complexity and improve learning efficiency. The adjustments in inputs reflect a key aspect of AI training, focusing on relevant data to enhance learning outcomes.

💡Vision square

The vision square concept is introduced in the video as a means to simplify the AI's perception by limiting its view to a smaller, more manageable area around the snake's head. This change reduces the input size from the entire game grid to a 20x20 pixel area, focusing the AI's 'attention' on immediate and relevant game elements, thus optimizing its performance.

💡Reward

A reward in AI and machine learning is feedback given to an algorithm, guiding it toward certain behaviors and away from others. In the video, the AI receives a reward when it successfully eats an apple, which encourages it to repeat behaviors that lead to this outcome. Conversely, penalties for hitting walls or itself teach the AI to avoid these actions.

💡Pong strategy

The 'Pong strategy' humorously refers to the AI's behavior when it moves aimlessly back and forth, similar to the paddle in the classic video game Pong. In the video, this strategy is shown as a phase of the AI's learning process, where it has not yet optimized its search strategy for apples, illustrating the iterative nature of AI learning and the occasional emergence of inefficient behaviors.

Highlights

A.I. is being taught to play Snake using Deep Q Learning, a type of reinforcement learning algorithm.

The creator has been away for three months but is back with a new project and updates.

The project involves creating an AI that can play Snake with the goal of being the 'god of snake walk'.

Q Learning is introduced as the algorithm of choice for teaching the AI to play the game.

The AI's vision is initially set to eight directions, but this is insufficient for achieving a perfect score.

To improve the AI's vision, the entire screen of pixels is given as input, allowing it to see as humans do.

A problem arises when the AI cannot differentiate between the snake's head and body, leading to self-collision.

The solution involves giving the AI a memory of the previous position, known as frame stacking.

The AI struggles with too many inputs, leading to a need to reduce the amount of information it processes.

The map is shrunk to reduce the number of pixels the AI needs to consider, improving its performance.

The AI is rewarded for getting food and punished for dying, a fundamental principle of Q Learning.

After 1,000 games, the AI begins to show an understanding of the game but still has room for improvement.

Further training leads to a more refined strategy, though the AI still gets trapped when the game state becomes complex.

After 5,000 games, the AI's performance plateaus, and the creator considers additional improvements.

The creator conducts research on other implementations of Q Learning for Snake, finding mixed results.

A simplified view of the game is tried, which improves performance but still falls short of a winning strategy.

The creator commits to making another video to continue the exploration of the perfect AI for Snake.

The video concludes with a promotion for a problem-solving website and app, 'Burning Dog', offering courses in computer science.

The creator apologizes for the long absence and shares upcoming plans for new videos and projects.