2048 AI Dominates | #SoME2
Table of Contents:
- Introduction
- Recreating 2048
- Creating the AI
- Teaching the AI
4.1. Parameters for Evaluation
4.2. Bill's Self-Teaching Process
- Implementing Minimax Algorithm
- Optimization and Testing
- Results of Bill's Performance
- Conclusion and Future Plans
Introduction
Playing the popular game 2048 better than most humans may seem like a challenging task for an AI. In this article, we will Delve into the process of creating an AI that can master 2048. We will go step by step, exploring the methods used to recreate the game, develop the AI, and train it to improve its gameplay. Additionally, we will discuss the implementation of the Minimax algorithm, optimization techniques, and the results obtained from our AI, named Bill. So let's begin this exciting Journey of creating a powerful AI for 2048.
Recreating 2048
The first step in creating our AI for 2048 was to recreate the game itself. We removed all the animations to allow the program to focus solely on making the AI perform better. We also addressed a bug with the tile spawning tutorial, which caused a tile to spawn even if the move didn't affect the board. Although we didn't fix this bug, we ensured that the AI only makes moves that directly impact the board position. With these modifications, we prepared the groundwork for creating our AI.
Creating the AI
Before we could start working on the AI, we needed a way for it to "see" the game board. To achieve this, we created a 2D array that served as a copy of what the player could visually see. This array allowed the AI to simulate tile spawns and movements, eliminating the need to extract data from the screen. We implemented move functions that operated on this array, moving the tiles as far as possible without merging them, merging them together, and then moving them again in their respective direction. With these functions in place, we were ready to dive into developing the AI.
Teaching the AI
We named our AI Bill and began the process of training it to play 2048. Our approach involved evaluating the game position and selecting moves with the highest evaluation. To determine the evaluation, we used various parameters, each assigned a multiplier. These parameters included highest tiles in the corner, number of empty tiles, tiles combined from a move, and score gained from a move.
To find the optimal multipliers for each parameter, we devised a system where Bill would change its multipliers and test their effectiveness. By increasing or decreasing the multipliers Based on performance, Bill learned to adapt and improve its gameplay. We recorded Bill's progress, iteratively adjusting the multipliers to achieve better results. This self-teaching process took into account both lucky and unlucky game scenarios, ensuring a balanced training experience for Bill.
Implementing Minimax Algorithm
To further enhance Bill's gameplay, we implemented the Minimax algorithm. Typically used in two-player games, we adapted it for 2048 by considering the tile spawns as a separate player. This allowed Bill to make decisions based on worst-case tile spawns while assuming the player made optimal moves. By simulating different moves and choosing the one with the best outcome, Bill could make intelligent decisions and optimize its gameplay.
Optimization and Testing
Throughout the development process, we encountered several flaws that hindered Bill's consistency. To address these issues, we made several alterations to Bill's training method. Firstly, we increased the number of games played to Gather more accurate data for evaluation. Additionally, we refined the process of changing multipliers by using a range of values and decreasing the range gradually to obtain precise multipliers. We also incorporated a mechanism to decrease the best score if Bill didn't Show improvement after playing a certain number of games.
Results of Bill's Performance
After extensive training and optimization, we evaluated Bill's performance based on the average and best scores obtained. Bill's average score ranged from thirty to thirty-three thousand, with the best score reaching Sixty thousand. The AI's ability to consistently reach the 2048 tile and occasionally even the 4096 tile was a significant achievement. We Are proud of the progress and improvements made by Bill throughout this project.
Conclusion and Future Plans
In conclusion, the process of creating an AI to play 2048 involved recreating the game, developing the AI, teaching it through a self-training mechanism, and implementing the Minimax algorithm. We encountered challenges along the way but managed to optimize and improve the AI's performance. Although the system had some flaws, we are content with the results achieved.
In the future, we plan to Continue working on Bill and further refine its gameplay. We are open to exploring new ideas and suggestions for improvement. Collaborating with others proved to be an effective way to generate ideas and overcome programming challenges. Overall, this project has taught us valuable lessons about AI development and the importance of collaboration in problem-solving.
Highlights:
- Creation of an AI to play 2048 better than most humans
- Recreating the 2048 game with necessary modifications for AI development
- Developing the AI named Bill and its ability to "see" the game board
- Teaching the AI through a self-training process and optimizing the multipliers
- Integration of the Minimax algorithm to enhance the AI's decision-making
- Optimization techniques and improvements made to Bill's performance
- Results showcasing Bill's average and best scores in the game
- Conclusion and future plans for further refinement of the AI's gameplay
FAQ:
Q: Can Bill consistently reach the 2048 tile?
A: Yes, after extensive training and optimization, Bill's performance shows consistent achievement of the 2048 tile.
Q: Does Bill improve its gameplay over time?
A: Yes, Bill utilizes a self-learning mechanism to adapt and improve its gameplay based on evaluation and testing of different multipliers.
Q: How does the Minimax algorithm work in 2048?
A: In 2048, the Minimax algorithm considers the tile spawns as a separate player and simulates different moves to choose the best outcome while assuming optimal moves from the player.
Q: Are there any future plans for Bill's development?
A: Yes, there are plans to continue refining Bill's gameplay and explore new ideas for improvement. Collaboration and suggestions from others are welcome in this process.
