2048 AI: Using Various Techniques to Train AI to Beat 2048
The 2048 game has been solved with techniques like Monte Carlo Tree Search, Expectimax, and Minimax, in which high scores can be attained in AI systems. These algorithms optimize the decision-making process by simulating moves and analyzing the outcome to make consistent wins in the game.
2048 games are popular puzzles wherein players combine different numbered tiles in a grid to form the number 2048 in a single tile. This simple challenge soon transforms into a highly strategic patience and foresight game. For most, reaching that 2048 tile represents the ultimate goal, but did you know that there is artificial intelligence that was trained to beat this game using different techniques? AI systems have been trained using systems such as Monte Carlo Tree Search (MCTS), Expectimax, and Minimax to optimize tile movements and score consistently high. Let's discuss how these algorithms work and solve the 2048 puzzle so amazingly well.
Monte Carlo Tree Search (MCTS)
Among the most powerful AI techniques that solve 2048 is Monte Carlo Tree Search. This algorithm simulates potential moves on the board game and evaluates the results based on random simulations. This uses the MCTS to construct a tree of possible moves, where every node represents a game state and the branch is a possible move. This algorithm runs a tremendous number of simulations for every possible action and picks the move that has the maximum average outcome over time. This is a very efficient approach for problems like 2048, as there are many possible moves and configurations of the board to be accounted for.
The reason why MCTS is so handy in 2048 is that it is able to account for the randomness involved in the game. Placing new tiles is predictable, making the board dynamic and difficult. However, MCTS can predict the most successful moves by simulating many potential future states of the game. Optimized variations of MCTS can drive the score into the thousands, sometimes more than 4096. This happens to be one of the most popular approaches for a 2048 AI because it brings together exploration and exploitation in nice harmony.
Expectimax Algorithm
Expectimax is yet another one of the most prominent algorithms in 2048 AI training. Unlike the consideration of the worst outcome given by Minimax, expectimax considers the moves through expectations by taking into account probability and, in the context of 2048, whether new tiles are to come in a certain location when a move is taken on a board. Expectimax doesn't look for the best move but calculates the expected value of each move taking into consideration the randomness of the new tile placement. The greatest strength of Expectimax is probability.
It takes into consideration every possible outcome by considering the values of the tiles and their positions on the board. Expectimax enables the AIs to find well-balanced decisions between the best obtainable outcome and probable distributions of the tiles, therefore, especially in game states that seem to be more complicated. Very good scores, mostly a score at 8192 and above, are seen when the algorithm is optimally developed to take account of the randomness of the game but yet play sagely.
Minimax Algorithm
The Minimax algorithm is a more traditional AI technique applied in many strategic decision-making applications. It works based on the evaluation of possible moves in terms of minimizing the worst-case scenario for the player. In 2048, Minimax assigns scores to each potential move based on tile values and board configuration. Then it selects a move that maximizes score while minimizing the chances of undesirable tile placements or low-value moves.
While Minimax does not check all the possibilities like MCTS and Expectimax, it is still a good algorithm when applied to 2048, especially in simple cases. Minimax can drive an AI to make good moves with scores of 2048 or 4096; however, it could suffer in the long term at which the game board tends to be more dynamic and full of unpredictability.
Ziap's 2048 AI
For those who want to see how such AI systems function, Ziap's 2048 AI is an interactive fun tool that displays how such algorithms play the game. In real-time, one can view the MCTS, Expectimax, and Minimax algorithms as they solve the puzzle in action. This is an excellent way of observing how AI decision-making works and more about the approach towards challenges such as 2048. Using techniques like Monte Carlo Tree Search, Expectimax, and Minimax, AI systems can solve the 2048 puzzle and achieve remarkable scores.
AI systems including Monte Carlo Tree Search, Expectimax, and Minimax have redefined approaches toward the game of 2048 for machines so that they obtain some fantastic score. These algorithms bring competitive depth into the game of 2048 with simulations over possible moves, their repercussions, and even potential losses of those moves. With advancements in AI, what will make these future techniques interesting is finding the new horizon that limits could be found to define 2048 themes much better; new strategies or ways in unlocking even better scores.
