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Select a .bbt file containing your saved training session to load.
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| βββββββ\ ______ ______ \ββ_______ \ββββββββ ______ \ββ _______
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| ββ ββ ββββββ\ \ββββββ\ ββ βββββββ\ | ββ | ββββββ\ ββ βββββββ
| βββββββ\ ββ \ββ/ ββ ββ ββ | ββ | ββ | ββ \ββ ββ\ββ \
| ββ__/ ββ ββ | βββββββ ββ ββ | ββ | ββ | ββ | ββ_\ββββββ\
| ββ ββ ββ \ββ ββ ββ ββ | ββ | ββ | ββ | ββ ββ
\βββββββ \ββ \βββββββ\ββ\ββ \ββ \ββ \ββ \ββ\βββββββ
In this application, we can teach a neural network to play Tetris. Will it learn to play better than a human? The goal of the application is to demonstrate the learning capabilities of neural networks, specifically the DQN neural network, and to understand the Q-Learning method.
Given a neural network and a Tetris game. During training, the network "sees" the game board:
The network's reward and punishment can be controlled based on the specified metrics of the game board and the goal, the number of lines cleared.
Tetris is not just a video game; it's a cultural phenomenon that has captivated players worldwide for more than four decades. Its simple yet addictive mechanics, as well as its ability to combine strategic thinking and quick reflexes, make it one of the most iconic and influential games in history.
Tetris was created in 1984 by Alexey Pajitnov, a Soviet software engineer. The idea originated from pentominoes, which are geometric shapes consisting of five identical squares. Pajitnov decided to work with shapes made of four squares, hence the "tetra" prefix, which means four in Greek. Combined with the Russian word "tennis," the name "Tetris" was formed.
The basic concept of the game is extremely simple: blocks of various shapes, called tetrominos, fall from the top of the screen. The player must rotate and place these blocks to create complete, gap-free lines at the bottom of the screen. As soon as a line is completed, it disappears, scoring points for the player and making room for new blocks. The game ends when the blocks reach the top of the screen, and there is no more space for new ones.
Tetris's addictive appeal lies in its ease of learning, but difficulty in mastering. As the game progresses, the blocks fall faster and faster, putting increasing pressure on the player to make quick and precise decisions. Successful gameplay requires not only fast fingers but also strategic planning. The player must think ahead, considering the shapes of the next few incoming blocks, and place the current block accordingly to maximize the chances of clearing lines.
Particularly important is the T-spin maneuver, where a T-shaped block is rotated into a tight space, giving special points. In addition, the Tetris clear β clearing four lines simultaneously with a straight "I" block β results in the highest score, and is often the goal of professional players.
Tetris achieved instant success in the Soviet Union, then quickly spread worldwide after being licensed and released on various platforms. With the launch of the Nintendo Game Boy handheld console in 1989, Tetris became explosively popular. The game is often cited as one of the main reasons the Game Boy was a huge success, selling millions of copies worldwide.
Over the years, Tetris has seen countless incarnations, appearing on almost every imaginable platform, from arcades to mobile phones, modern consoles, and PCs. Competitions are held, it is the subject of scientific research (for example, the phenomenon known as the "Tetris effect," where people see blocks in their minds even while sleeping), and it has become deeply embedded in popular culture.
Tetris's enduring appeal lies in its universality. No language knowledge is needed to understand it, and despite its simplicity, it can be endlessly deep and challenging. This is the kind of game that can be played for five minutes on a bus, or for hours at a time, immersed in the meditative rhythm of arranging blocks. Tetris is not just a game; it's an enduring puzzle that has entertained and challenged people for generations, and will likely remain with us for a very long time.
The application offers three distinct game modes:
In this game mode, we can try out Tetris games playable by a human. Control is possible with the arrow keys and the space bar.
We get points for dropped elements and cleared lines. After a certain number of cleared lines, the game levels up, and the falling speed of the elements increases. If the game board (stack) fills up, the game ends.
This mode provides an opportunity to train a neural network with the specified parameters. The network receives visual input from the game board, including fallen elements, the position and orientation of the current element, the holes that have formed, and the next piece. The network's reward and punishment depend on the game board's metrics and the number of lines cleared.
The Train mode includes the following sub-menu items:
This game mode allows observing the trained neural network's gameplay in real conditions. If you hold down the down arrow key during the network's play, the network's game speeds up.
The running process of any game mode can be interrupted with the stop function. The system then switches to idle, waiting for further commands.
Clicking the [Options] button opens a modal window where parameters for training the neural network can be set. These parameters influence the reward weights and training behavior.
Positive parameter values indicate reward, negative values indicate punishment. When designing the reward system, we can tune the following characteristics:
Moving average windows help us observe the long-term direction of the network's development. If progress stalls or deteriorates over a longer number of games, the network has not found a solution with the current reward system settings.
The ratio of these two moving averages indicates the direction of progress.
When training mode is active, the "AI Panel" appears on the screen, providing real-time feedback on the network's performance and the game's status.
Shows how the neural network "sees" the game board.
We can see the currently set values of the network's reward system.
Well, here we are at the end of an exciting journey, where the simple yet profound world of Tetris met the complex challenges of artificial intelligence. With the BrainTris application, my intention was not just to create a game, but also a laboratory where we can explore the power of Q-Learning and the learning capabilities of neural networks in practice.
I hope that the tools and detailed documentation I have provided will inspire you to experiment. Feel free to modify the reward system parameters, observe how different parameters influence the network's behavior, and discover what strategies the AI adopts to master Tetris.
I wish you much success in your experiments and in creating a neural network that plays as perfectly as possible! I trust that this application will not only provide entertainment but also a valuable learning opportunity in the fascinating field of artificial intelligence and machine learning. Explore the possibilities, and let BrainTris help you understand how an AI thinks and learns!
Thank you for trying to create a smart artificial intelligence:
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