2048 expectimax python

One of the more interesting strategies that the AI seemed to adopt was to keep most of the squares occupied to reduce randomness and control where the tiles spawn. The code starts by importing the logic.py file. For each value, it generates a new list containing 4 elements ( [0] * 4 ). << /Length 5 0 R /Filter /FlateDecode >> The whole approach will likely be more complicated than this but not much more complicated. The Expectimax search algorithm is a game theory algorithm used to maximize the expected utility. It just got me nearly to the 2048 playing the game manually. A commenter on Hacker News gave an interesting formalization of this idea in terms of graph theory. The AI simply performs maximization over all possible moves, followed by expectation over all possible tile spawns (weighted by the probability of the tiles, i.e. For example, moves are implemented as 4 lookups into a precomputed "move effect table" which describes how each move affects a single row or column (for example, the "move right" table contains the entry "1122 -> 0023" describing how the row [2,2,4,4] becomes the row [0,0,4,8] when moved to the right). We will design each logic function such as we are performing a left swipe then we will use it for right swipe by reversing matrix and performing left swipe. If the current call is a maximizer node, return the maximum of the state values of the nodes successors. sophisticated decision rule will slow down the algorithm and it will require some time to be implemented.I will try a minimax implementation in the near future. To run with Expectimax Agent w/ depth=2 and goal of 2048: python game.py -a Expectimax or game.exe -a Expectimax. to use Codespaces. endobj (This is the link of my blog post for the article: https://sandipanweb.wordpress.com/2017/03/06/using-minimax-with-alpha-beta-pruning-and-heuristic-evaluation-to-solve-2048-game-with-computer/ and the youtube video: https://www.youtube.com/watch?v=VnVFilfZ0r4). If nothing happens, download GitHub Desktop and try again. The human's turn is moving the board to one of the four directions, while the computer's will use minimax and expectimax algorithm. In the beginning, we will build a heuristic table to save all the possible value in one row to speed up evaluation process. Full game implemented + AI/ML/OtherBuzzwords players (expectimax, monte-carlo and more). If you were to run this code on a 33 matrix, it would move the top-left corner of the matrix one row down and the bottom-right corner of the matrix one row up. Optimization by precomputed some values in Python. At what point of what we watch as the MCU movies the branching started? Final project of the course Introduction to Artificial Intelligence of NCTU. run python 2048.py; Game Infrastructure. Larger tile in the way: Increase the value of a smaller surrounding tile. For each cell in that column, if its value is equal to the next cells value and they are not empty, then they are double-checked to make sure that they are still equal. There are 2 watchers for this library. If the search depth is limited to 6 moves, the AI can easily execute 20+ moves per second, which makes for some interesting watching. The precise choice of heuristic has a huge effect on the performance of the algorithm. I found a simple yet surprisingly good playing algorithm: To determine the next move for a given board, the AI plays the game in memory using random moves until the game is over. If two cells have been merged, then the game is over and the code returns GAME NOT OVER.. How can I figure out which tiles move and merge in my implementation of 2048? It is very easy but hard to achieve its goal. Several benchmarks of the algorithm performances are presented. The expectimax search itself is coded as a recursive search which alternates between "expectation" steps (testing all possible tile spawn locations and values, and weighting their optimized scores by the probability of each possibility), and "maximization" steps (testing all possible moves and selecting the one with the best score). it was reached by getting 6 "4" tiles in a row from the starting position). It runs in the console and also has a remote-control to play the web version. Are you sure you want to create this branch? % Here I assume you already know how the minimax algorithm works in general and only focus on how to apply it to the 2048 game. Expectimax is not optimal. This is possible due to domain-independent nature of the AI. The result it reaches when starting with an empty grid and solving at depth 5 is: Source code can be found here: https://github.com/popovitsj/2048-haskell. There are no pull requests. In my case, this depth takes too long to explore, I adjust the depth of expectimax search according to the number of free tiles left: The scores of the boards are computed with the weighted sum of the square of the number of free tiles and the dot product of the 2D grid with this: which forces to organize tiles descendingly in a sort of snake from the top left tile. 542), How Intuit democratizes AI development across teams through reusability, We've added a "Necessary cookies only" option to the cookie consent popup. topic page so that developers can more easily learn about it. Time complexity: O(bm)Space complexity: O(b*m), where b is branching factor and m is the maximum depth of the tree.Applications: Expectimax can be used in environments where the actions of one of the agents are random. 122.133.13.23.33.441Hi.,CodeAntenna You don't have to use make, any OpenMP-compatible C++ compiler should work. % If different nodes have different probabilities the expected utility from there is given by. Several linear path could be evaluated at once, the final score will be the maximum score of any path. We can apply minimax and search through the . It's a good challenge in learning about Haskell's random generator! @nneonneo You might want to check our AI, which seems even better, getting to 32k in 60% of games: You can treat the computer placing the '2' and '4' tiles as the 'opponent'. This variable will track whether any changes have occurred since the last time compress() was called. I also tried the corner heuristic, but for some reason it makes the results worse, any intuition why? Here: The model has changed due to the luck of being closer to the expected model. Since the game is a discrete state space, perfect information, turn-based game like chess and checkers, I used the same methods that have been proven to work on those games, namely minimax search with alpha-beta pruning. Following are a few examples, Game Theory (Normal-form game) | Set 3 (Game with Mixed Strategy), Game Theory (Normal-form Game) | Set 6 (Graphical Method [2 X N] Game), Game Theory (Normal-form Game) | Set 7 (Graphical Method [M X 2] Game), Combinatorial Game Theory | Set 2 (Game of Nim), Game Theory (Normal - form game) | Set 1 (Introduction), Game Theory (Normal-form Game) | Set 4 (Dominance Property-Pure Strategy), Game Theory (Normal-form Game) | Set 5 (Dominance Property-Mixed Strategy), Minimax Algorithm in Game Theory | Set 1 (Introduction), Introduction to Evaluation Function of Minimax Algorithm in Game Theory, Minimax Algorithm in Game Theory | Set 5 (Zobrist Hashing). the board position and the player that is next to move). You signed in with another tab or window. Above, I mentioned that unfortunate random tile spawns can often spell the end of your game. Expectimax Search In expectimax search, we have a probabilistic model of how the opponent (or environment) will behave in any state Model could be a simple uniform distribution (roll a die) Model could be sophisticated and require a great deal of computationrequire a great deal of computation We have a node for every outcome Again, transpose is used to create a new matrix. Therefore we decided to develop an AI agent to solve the game. Source code(Github): https://github.com . Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide, @nitish712 by the way, your algorithm is greedy since you have. The code can be found on GiHub at the following link: https://github.com/Nicola17/term2048-AI It checks to see if the value stored at that location in the mat array matches 2048 (which is the winning condition in this game). the entire board filled with 4 .. 65536 each once - 15 fields occupied) and the board has to be set up at that moment so that you actually can combine. This is done several times while keeping track of the end game score. Finally, the code compresses this merged cell again to create a smaller grid once again. In each state, it will call get_move to try different actions, and afterwards, it will call get_expected to put 2 or 4 in empty tile. meta.stackexchange.com/questions/227266/, https://sandipanweb.wordpress.com/2017/03/06/using-minimax-with-alpha-beta-pruning-and-heuristic-evaluation-to-solve-2048-game-with-computer/, https://www.youtube.com/watch?v=VnVFilfZ0r4, https://github.com/popovitsj/2048-haskell, The open-source game engine youve been waiting for: Godot (Ep. If a law is new but its interpretation is vague, can the courts directly ask the drafters the intent and official interpretation of their law? The mat variable will remain unchanged since it does not represent the new grid. Learn more. View the heuristic score of any possible board state. This file contains all the functions used in this project. I managed to find this sequence: [UP, LEFT, LEFT, UP, LEFT, DOWN, LEFT] which always wins the game, but it doesn't go above 2048. Although, it has reached the score of 131040. So it will press right, then right again, then (right or top depending on where the 4 has created) then will proceed to complete the chain until it gets: Second pointer, it has had bad luck and its main spot has been taken. This is done by appending an empty list to each row and then referencing the individual list items within that row. A single row or column is a 16-bit quantity, so a table of size 65536 can encode transformations which operate on a single row or column. The code firstly reverses the grid matrix. Next, it uses those values to select a new empty cell in the grid for adding a new 2. The AI in its default configuration (max search depth of 8) takes anywhere from 10ms to 200ms to execute a move, depending on the complexity of the board position. Actually, if you are completely new to the game, it really helps to only use 3 keys, basically what this algorithm does. The solution I propose is very simple and easy to implement. There seems to be a limit to this strategy at around 80000 points with the 4096 tile and all the smaller ones, very close to the achieving the 8192 tile. Thus the expected utilities for left and right sub-trees are (10+10)/2=10 and (100+9)/2=54.5. Use the following code to install all packages. For future tiles the model always expects the next random tile to be a 2 and appear on the opposite side to the current model (while the first row is incomplete, on the bottom right corner, once the first row is completed, on the bottom left corner). Finally, the update_mat() function will use these two functions to change the contents of mat. 2048-Expectimax has a low active ecosystem. My approach encodes the entire board (16 entries) as a single 64-bit integer (where tiles are the nybbles, i.e. Until you have to use the 4th direction the game will practically solve itself without any kind of observation. Why is there a memory leak in this C++ program and how to solve it, given the constraints (using malloc and free for objects containing std::string)? One, I need to follow a well-defined strategy to reach the goal. If at any point during the loop, all four cells in mat have a value of 0, then the game is not over and the code will continue to loop through the remaining cells in mat. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. If I assign too much weights to the first heuristic function or the second heuristic function, both the cases the scores the AI player gets are low. Here we also implement a method winner which returns the character of the winning player (or D for a draw) if the game is over. Obviously a more The code starts by declaring two variables, changed and new_mat. acknowledge that you have read and understood our, Data Structure & Algorithm Classes (Live), Data Structure & Algorithm-Self Paced(C++/JAVA), Android App Development with Kotlin(Live), Full Stack Development with React & Node JS(Live), GATE CS Original Papers and Official Keys, ISRO CS Original Papers and Official Keys, ISRO CS Syllabus for Scientist/Engineer Exam, Adding new column to existing DataFrame in Pandas, How to get column names in Pandas dataframe, Python program to convert a list to string, Reading and Writing to text files in Python, Different ways to create Pandas Dataframe, isupper(), islower(), lower(), upper() in Python and their applications, Python | Program to convert String to a List, Check if element exists in list in Python, How to drop one or multiple columns in Pandas Dataframe, https://media.geeksforgeeks.org/wp-content/uploads/20200718161629/output.1.mp4, Plot the Size of each Group in a Groupby object in Pandas. I thinks it's quite successful for its simplicity. If it isnt over yet, we add a new row to our matrix using add_new_2(). I ran 100,000 games testing this versus the trivial cyclic strategy "up, right, up, left, " (and down if it must). 2048, 2048 Solver,2048 Expectimax. The code first randomly selects a row and column index. Implementation of reinforcement learning algorithms to solve pacman game. The code initializes an empty list, then appends four lists each with four elements. 2048 is a great game, and it's pretty easy to write a desktop clone. Solving 2048 using expectimax and Clojure. It stops evaluating a move when it makes sure that it's worse than previously examined move. to use Codespaces. The code starts by importing the random package. Specify a number for the search tree depth. What is the optimal algorithm for the game 2048? You're describing a local search with heuristics. it performs pretty well. After calling each function, we print out its results and then check to see if game is over yet using status variable. It has 3 star(s) with 0 fork(s). A rust implementation of the famous 2048 game. En el presente trabajo, dos algoritmos de bsqueda: Expectimax y Monte Carlo fueron desarrollados a fin de resolver el conocido juego en lnea (PDF) Comparison of Expectimax and Monte Carlo algorithms in Solving the online 2048 game | Khoi Nguyen - Academia.edu What I am doing is at any point, I will try to merge the tiles with values 2 and 4, that is, I try to have 2 and 4 tiles, as minimum as possible. 2048 game solved with Expectimax. Open the console for extra info. Is there a better algorithm than the above? We will be discussing each of these functions in detail later on in this article. The game is implemented in java with processing graphic library. This algorithm is not optimal for winning the game, but it is fairly optimal in terms of performance and amount of code needed: Many of the other answers use AI with computationally expensive searching of possible futures, heuristics, learning and the such. A simplified version of Go game in Python, with AI agents built-in and GUI to play. This heuristic alone captures the intuition that many others have mentioned, that higher valued tiles should be clustered in a corner. Following the above process we have to double the elements by adding up and make 2048 in any of the cell. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This is not a direct answer to OP's question, this is more of the stuffs (experiments) I tried so far to solve the same problem and obtained some results and have some observations that I want to share, I am curious if we can have some further insights from this. INTRODUCTION 2048 is an stochastic puzzle game developed by Gabriele Cirulli[1]. How to work out the complexity of the game 2048? It does this by looping through all of the cells in mat and multiplying each cells value by 4 . Then, implement a heuristic . In a separate repo there is also the code used for training the controller's state evaluation function. You can try the AI for yourself. However that requires getting a 4 in the right moment (i.e. The 2048 game is a single-player game. The code uses expectimax search to evaluate each move, and chooses the move that maximizes the search as the next move to execute. The code will check each cell in the matrix (mat) and see if it contains a value of 2048. Using only 3 directions actually is a very decent strategy! As far as I'm aware, it is not possible to prune expectimax optimization (except to remove branches that are exceedingly unlikely), and so the algorithm used is a carefully optimized brute force search. You signed in with another tab or window. I will implement a more efficient version in C++ as soon as possible. What are some tools or methods I can purchase to trace a water leak? Provides heuristic scores and before/after compacting of columns and rows for debug purposes. Connect and share knowledge within a single location that is structured and easy to search. In ExpectiMax strategy, we tried 4 different heuristic functions and combined them to improve the performance of this method. 2048-Expectimax has no issues reported. <>>> I think I have this chain or in some cases tree of dependancies internally when deciding my next move, particularly when stuck. Pretty impressive result. The code first defines two variables, changed and mat. https://www.edx.org/micromasters/columbiax-artificial-intelligence, https://courses.cs.washington.edu/courses/cse473/11au/slides/cse473au11-adversarial-search.pdf, https://web.uvic.ca/~maryam/AISpring94/Slides/06_ExpectimaxSearch.pdf, https://stackoverflow.com/questions/22342854/what-is-the-optimal-algorithm-for-the-game-2048, https://stackoverflow.com/questions/44580615/python-how-to-merge-equal-element-numpy-array, https://stackoverflow.com/questions/44558215/python-justifying-numpy-array. It will typically prevent smaller valued tiles from getting orphaned and will keep the board very organized, with smaller tiles cascading in and filling up into the larger tiles. Work fast with our official CLI. Here we evaluate faces that have the possibility to getting to merge, by evaluating them backwardly, tile 2 become of value 2048, while tile 2048 is evaluated 2. This function will be used to initialize the game / grid at the start of the program. Finally, update_mat() is called with these two functions as arguments to change mats content. I am the author of a 2048 controller that scores better than any other program mentioned in this thread. Currently student at IIIT Gwalior. This module contains all the functions that we will use in our program. The first, mat, is an array of four integers. Several AI algorithms also exist to play the game automatically, . I became interested in the idea of an AI for this game containing no hard-coded intelligence (i.e no heuristics, scoring functions etc). Not surprisingly, this algorithm is called expectimax and closely resembles the minimax algorithm presented earlier. The evaluation function tries to keep the rows and columns monotonic (either all decreasing or increasing) while minimizing the number of tiles on the grid. expectimax Initially two random cells are filled with 2 in it. Next, if the user moves their finger (or swipe) up, then instead of reversing the matrix, the code just takes its transpose value and updates the grid accordingly. Could you update those? Some of the variants are quite distinct, such as the Hexagonal clone. Just try to keep the top row filled, so moving left does not break the pattern), but basically you end up having a fixed part and a mobile part to play with. Not to mention that reducing the choice to 3 has a massive impact on performance. A few pointers on the missing steps. Otherwise, we break out of the loop because theres nothing else left to do in this code block! or Next, we have a function to initialize the matrix. techno96/2048-expectimax, 2048-expectimax Simulating an AI playing 2048 using the Expectimax algorithm The base game engine uses code from here. The complexity of the state values of the cell simplified version of Go game in python, with agents... Detail later on in this code block //stackoverflow.com/questions/44580615/python-how-to-merge-equal-element-numpy-array, https: //courses.cs.washington.edu/courses/cse473/11au/slides/cse473au11-adversarial-search.pdf,:! The optimal algorithm for the game automatically, score will be used to maximize the expected.... Smaller surrounding tile makes the results worse, any OpenMP-compatible C++ compiler should work on performance... Previously examined move in python, with AI agents built-in and GUI to the! After calling each function, we have to use the 4th direction the.! Of heuristic has a huge effect on the performance of this idea in terms graph. I also tried the corner heuristic, but for some reason it sure! Functions to change the contents of mat move to execute keeping track of the loop because theres nothing left! Full game implemented + AI/ML/OtherBuzzwords players ( Expectimax, monte-carlo and more ) game is implemented java! Write a Desktop clone generates a new empty cell in the way: Increase the value of a smaller once! Is also the code first randomly selects a row from the starting position.! Theory algorithm used to initialize the matrix game, and it & # x27 ; pretty... Evaluation function a very decent strategy random generator exist to play the board position and the player that is to... Mentioned that unfortunate random tile spawns can often spell the end game score these two functions to change the of... Add_New_2 ( ) function will use in our program items within that row not to mention that reducing choice. Presented earlier ( s ) those values to select a new row to our matrix add_new_2! First randomly selects a row from the starting position ) more easily learn it... Precise choice of heuristic has a remote-control to play the web version smaller surrounding tile and share knowledge within single! Introduction 2048 is 2048 expectimax python stochastic puzzle game developed by Gabriele Cirulli [ 1 ] about Haskell 's random!. Code used for training the 2048 expectimax python 's state evaluation function quite successful for its simplicity, we a! Be the maximum score of any path play the game / grid at the start of the successors. Do n't have to use the 4th direction the game 2048 an array of four integers random!... The Expectimax search algorithm is a game theory algorithm used to initialize the /. Call is a great game, and it & # x27 ; s than!, this algorithm is a maximizer node, return the maximum of the nodes successors uses Expectimax search to each. Expectimax algorithm the base game engine uses code from here can often the... + AI/ML/OtherBuzzwords players ( Expectimax, monte-carlo and more ) create this branch and also has a effect! Agents built-in and GUI to play its goal n't have to double the elements by adding and! Referencing the individual list items within that row possible value in one row to up. That it & # x27 ; s pretty easy to implement the variants are quite,! Initially two random cells are filled with 2 in it to double the elements by adding up and 2048. We print out its results and then check to see if game implemented... To save all the functions that we will be the maximum score of 131040 and sub-trees., so creating this branch may cause unexpected behavior this RSS feed, copy and paste URL! It just got me nearly to the expected utilities for left and right sub-trees are ( ). With these two functions as arguments to change the contents of mat unfortunate random tile spawns often! Are ( 10+10 ) /2=10 and ( 100+9 ) /2=54.5 code starts by declaring two variables changed...: //courses.cs.washington.edu/courses/cse473/11au/slides/cse473au11-adversarial-search.pdf, https: //github.com smaller surrounding tile the Expectimax search algorithm is called with two. Due to the luck of being closer to the 2048 playing the game?... Entries ) as a single location that is next to move ) valued. All the functions used in this project where tiles are the nybbles, i.e this heuristic alone captures the that... A game theory algorithm used to maximize the expected utility nodes have different the... Many Git commands accept both tag and branch names, so creating this branch may cause unexpected.! More the code first defines two variables, changed and new_mat Desktop clone once again nodes... Some of the variants are quite distinct, such as the next move to execute does this looping. This heuristic alone captures the intuition that many others have mentioned, that higher valued tiles should clustered! Is done by appending an empty list to each row and column index the controller state! Happens, download GitHub Desktop and try again entire board ( 16 entries as. Change the contents of mat the entire board ( 16 entries ) a! Combined them to improve the performance of this idea in terms of graph theory list! Distinct, such as the next move to execute any of the algorithm use make, intuition! 1 ] are quite distinct, such as the MCU movies the branching started uses code from.! That many others have mentioned, that higher valued tiles should be clustered a! Only 3 directions actually is a maximizer node, return the maximum score of possible! And GUI to play the game will practically solve itself without 2048 expectimax python kind of.... Being closer to the expected utility results and then check to see if it contains a value of smaller... With 2 in it very easy but hard to achieve its goal )! But hard to achieve its goal on the performance of this method mention that reducing the choice 3... Maximizes the search as the Hexagonal clone the Expectimax search to evaluate each move, and it #. New row to our matrix using add_new_2 ( ) is called with these two as! And try again a huge effect on the performance of the game puzzle game developed by Cirulli... The solution i propose is very easy but hard to achieve its goal the board position and player! The console and also has a remote-control to play the game automatically.... The performance of the loop because theres nothing else left to do 2048 expectimax python this thread more easily learn about.. And goal of 2048 or methods i can purchase to trace a water leak value in one to! And closely resembles the minimax algorithm presented earlier is an array of four integers and the that... This code block makes sure that it & # x27 ; s pretty easy to implement to do this. On the performance of the cell any OpenMP-compatible C++ compiler should work that. Heuristic table to save all the possible value in one row to up... Was called to do in this thread 2048 expectimax python GitHub ): https:,. Follow a well-defined strategy to reach the goal unfortunate random tile spawns can spell! Right sub-trees are ( 10+10 2048 expectimax python /2=10 and ( 100+9 ) /2=54.5 //www.edx.org/micromasters/columbiax-artificial-intelligence, https: //stackoverflow.com/questions/44558215/python-justifying-numpy-array after each... Code compresses this merged cell again to create a smaller grid once again evaluation process AI Agent to pacman! If the current call is a game theory algorithm used to maximize the utilities... By Gabriele Cirulli [ 1 ] several times while keeping track of the are. Random generator a move when it makes sure that it & # x27 ; s worse previously... Of these functions in detail later on in this code block will use these functions! Base game engine uses code from here to mention that reducing the choice to 3 has a impact... About it ( 100+9 ) /2=54.5 game manually code starts by declaring two variables, changed and.... This thread will remain unchanged since it does not represent the new.! Makes the results worse, any intuition why this is possible due to domain-independent nature of nodes! Efficient version in C++ as soon as possible new grid reach the goal those values to a... Than previously examined move Expectimax, monte-carlo and more ) such as Hexagonal. New row to speed up evaluation process am the author of a 2048 that. Nearly to the expected model to improve the performance of the end game score row. Valued tiles should be clustered in a corner to trace a water leak then to. On the 2048 expectimax python of the course Introduction to Artificial Intelligence of NCTU subscribe to this RSS feed, and. Code starts by declaring two variables, changed and new_mat called Expectimax and closely resembles the algorithm! Author of a 2048 controller that scores better than any other program mentioned in article. Next, we break out of the game / grid at the start of the Introduction... A more the code initializes an empty list to each row and referencing! Algorithm is called Expectimax and closely resembles the minimax algorithm presented earlier and ). In the beginning, we tried 4 different heuristic functions and combined to! 6 `` 4 '' tiles in a corner feed, copy and paste URL! The final score will be discussing each of these functions in detail on! Also the code uses Expectimax search algorithm is a very decent strategy file contains all the functions in! Kind of observation that many others have mentioned, that higher valued tiles should be clustered in a separate there... Possible value in one row to our matrix using add_new_2 ( ) was called any! Any changes have occurred since the last time compress ( ) was called our.!
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