Search anything:

Tic Tac Toe with Reinforcement Learning

Internship at OpenGenus

Get this book -> Problems on Array: For Interviews and Competitive Programming

Tic Tac Toe is one of the most popular game which needs only two players to play it. This classic game is developed with almost every well-known programming language. In this article, the game is developed using Reinforcement Learning.


A machine learning area that trains models or agents to act on their own to complete tasks is called reinforcement learning. This training method teaches the agent through rewarding it for any desired acts, and punishing it for undesired ones. Based on this reward or punishment, the agent learns to take the most efficient action.

A tic tac toe game is a classic two-player game where on a 3X3 board, one player can put 'X' symbol and the other can put 'O' symbol. The players can choose any box on the board as long as the box is empty. The players take turns to put the symbols in order to play. To win the game, one of them has to match three same symbols in a row, or a column, or a diagonal line.

For example,

|X|O| |
| |X| |
| |O|X|

Here, the 'X' player wins. The game is over when any player wins or the board is full. If no one wins, then the game is draw.

Project Description

In this project, we have trained one agent to learn from reward and punishment. For winning the game, the reward is +1, and for losing the punishment is -1. This agent will use the symbol 'X'.

We have used Q-learning method of reinforcement learning to train this player. Using this method, the agent can find the best move based on the current move in a specific environment. It takes the action that has the best possiblity of maximizing the reward. To learn more about Q-learning click here.

Another player with the symbol 'O', will randomly choose an empty box for the input. The game will reset when the one player wins or it is draw.

Start The Project

We begin by importing the necessary libraries.

import numpy as np
import random
import time
from tkinter import *
import matplotlib.pyplot as plt
from IPython import display

We use Tkinter library of Python for this project. Tkinter provides grid() method that will help with accessing the boxes on the board. Also, tkinter methods are used for updating the board values after each input.

Building X-Player

X-player can only use the 'X' symbol. At the beginning it chooses a random action, but as the game progresses it calculates the best move based on the rewards and punishments it gets.

class X_Agent:
    def __init__(self):
        self.epsilon = 0.1  
        self.lr = 0.001 
        self.state_history = [] 

    def initialize_V(self, env, state_winner_triples):
        # initialize V
        # if agent wins, V(s) = 1
        # if agent loses or draw V(s) = 0
        # otherwise V(s) = 0.5
        V = np.zeros(env.max_states)
        for state, winner, gameOver in state_winner_triples:
            if gameOver:
                if winner == env.x:  
                    state_value = 1
                elif winner == env.o:  
                    state_value = 1
                    state_value = 0
                state_value = 0.5

            V[state] = state_value
        self.V = V

    def set_symbol(self, symbol):
        self.symbol = symbol

    def reset_history(self):
        self.state_history = []

    def choose_random_action(self, env):
        empty_moves = env.get_empty_moves()
        # select randomly from possible moves
        random_index_from_empty_moves = np.random.choice(len(empty_moves))
        next_random_move = empty_moves[random_index_from_empty_moves]
        return next_random_move

    def choose_best_action_from_states(self, env):
        next_best_move, best_state = env.get_next_best_move(self)
        return next_best_move, best_state

We define the learning rate for the agent and a list to store the best states of the agent. These methods are used for checking empty boxes, choosing random actions, and choosing best actions. We initialize the state values according to the states of the game.

The symbol is set at the beginning of the class and it chooses a random box if the game is only starting, or the box that leads to winning.

    def get_next_move(self, env):
        next_best_move, best_state = None, None
        random_number = np.random.rand()  # will give a random float between 0 and 1
        if random_number < self.epsilon:
            # take a random action
            next_best_move = self.choose_random_action(env)
            next_best_move, best_state = self.choose_best_action_from_states(env)
        return next_best_move, best_state

    def take_action(self, env):
        selected_next_move, best_state = self.get_next_move(env)
        # make next move
        env.board[selected_next_move[0], selected_next_move[1]] = self.symbol
        for  i in self.state_history:
            if best_state > self.state_history[i]:
                self.state_history[i] = best_state

    def update_state_history(self, state):

    def update(self, env):
        # V(prev_state) = V(prev_state) + lr * ( V(next_state) - V(pre_state) ), where V(next_state) is reward if its most current state

        reward = env.reward(self.symbol)
        target = reward
        for prev in reversed(self.state_history):
            value = self.V[prev] + self.lr * (target - self.V[prev])
            self.V[prev] = value
            target = value

We use the reward, states of the game, states of the X-player to find the most efficient moves for the X-player. The epsilon value is used for reducing the randomness of the X-player. We update or reset the values as the games continues.

Building O-Player

The O-player only uses the 'O' symbol to play. This is called an agent, but it doesn't have the same functions as X-player. It chooses random values and finds an empty box for the input.

class O_Agent:

    def set_symbol(self, symbol):
        self.symbol = symbol

    def take_action(self, env):
        while True:
                i = random.randint(0,2)
                j = random.randint(0,2)
                if env.is_empty(i, j):
                    env.board[i, j] = self.symbol
                print("Please enter valid move")

The symbol is set as the X-player and a method chooses random values for rows and columns to put the symbol. This method keeps running until an empty spot is found.

Building The Environment

In this section, we create the gameboard, and use methods to perform other tasks. We assign +1 value to indicate X-player and -1 value to indicate O-player throughout the game. We also assign initial values for the winner, gameover, and maximum possible states for the game.

class Game():

    def __init__(self):
        self.board = np.zeros((3, 3))  # make an 2D array with zeros, zero means the box is empty
        self.x = 1  # player 1
        self.o = -1  # player 2
        self.winner = None  # initially there is no winner
        self.gameOver = False  # game is not gameOver initially
        self.max_states = 3 ** (3 * 3)  # =19683, total number of possible states for tic tac toe game
        self.root = Tk()

    def is_empty(self, i, j):
        return self.board[i, j] == 0

    def reward(self, symbol):
        collected_reward = 0
        if self.game_over() and self.winner == symbol:  
            collected_reward = 1
            collected_reward = -1
        return collected_reward

    def is_draw(self):
        is_draw = False
        if self.gameOver and self.winner is None: 
            is_draw = True
        return is_draw

At the beginning, the winner is set to None, and the gameOver is False. We also initialize Tkinter and the grid method here. We define methods to find empty boxes, check for draw, and assign reward or punishment to the player.

    def get_state(self):
        # returns the current state represented as an integer
        # from 0...|S|-1 where S = set of all possible states ie |S| = 3^3, since each box can have three possible values 0(empty), x, o
        # this is like finding the integer represented by a base-3 number
        state = 0
        loop_index = 0
        for i in range(3):
            for j in range(3):
                if self.board[i, j] == self.x:
                    state_value = 1
                elif self.board[i, j] == self.o:
                    state_value = 2
                    state_value = 0  # empty

                state += (3 ** loop_index) * state_value
                loop_index += 1
        return state

    def game_over(self):
        if self.gameOver:  
            return True  # game is over

        players = [self.x, self.o]

        # check if there are any same symbols on rows side
        for i in range(3):
            for player in players:
                if self.board[i].sum() == player * 3:  # results will be  1+1+1 = 3 for player o and -1-1-1 = -3 for player x
                    self.winner = player
                    self.gameOver = True
                    return True  # game is over

        # check if there are any same symbols on columns side
        for j in range(3):
            for player in players:
                if self.board[:, j].sum() == player * 3:
                    self.winner = player
                    self.gameOver = True
                    return True  # game is over

        # finally if there is no same symbols on either rows or columns we check on diagonal sides
        for player in players:
            # top-left -> bottom-right diagonal
            # trace() function Return the sum along diagonals of the array
            if self.board.trace() == player * 3:
                self.winner = player
                self.gameOver = True
                return True  # game is over

            # top-right -> bottom-left diagonal
            if np.fliplr(self.board).trace() == player * 3:
                self.winner = player
                self.gameOver = True
                return True  # game is over

        # np.all() function Test whether all array elements along a given axis evaluate to True.
        # self.board == 0 this will convert all positions of board to True or False, True if equal to 0 False if not
        board_with_true_false = self.board == 0
        if np.all(board_with_true_false == False):
            # game is draw hence there is no winner
            self.winner = None
            self.gameOver = True
            return True  # game is over

        # finally if game is not over
        self.winner = None
        return False

A method is defined to find the state of the agent. This is used to get the next best move for the agent. The game_over function checks if any player wins or the board is full. The players will win if they match 3 of their symbols diagonally, in the same row or in the same column. Otherwise, the game will go on until the players run out of empty spaces.

    def get_empty_moves(self):
        empty_moves = []
        # we will be looping to all 9 boxes, and collecting possible moves which are empty
        for i in range(3):
            for j in range(3):
                if self.is_empty(i, j):  # check if this box is empty or not
                    empty_moves.append((i, j))
        return empty_moves

    def get_next_best_move(self, agent):
        best_value = -1  # lets initialize with something lower
        next_best_move = None
        best_state = None
        for i in range(3):
            for j in range(3):
                if self.is_empty(i, j):
                    # lets make this move and check what will be the state if we choose this move ie, (i, j) move, we we will revert it back after getting state
                    self.board[i, j] = agent.symbol
                    state = self.get_state() 
                    self.board[i, j] = 0  # revert back to empty state ie actual state
                    if agent.V[state] > best_value:
                        best_value = agent.V[state]
                        best_state = state
                        next_best_move = (i, j)

        return next_best_move, best_state

We create a list for the empty moves that are possible. To find the nest best move, we initialize something lower to the best state at the start. This value changes if the agent has found a better state. The next best move is the row and column number of that new best state.

    def draw_board(self):
        #making grid
        self.squares = []
        for i in range(3):
            row = []
            for j in range(3):
                square_frame = Frame(
                square_frame.grid(row=i, column=j, padx=4, pady=4)
                square = Label(self.root, bg='white', text=' ')
                square.grid(row=i, column=j)
                square_data = {"frame": square_frame, "number": square}

    def update_board(self):
        for i in range(3):
            for j in range(3):
                if self.board[i, j] == self.x:
                            font=('Arial', 50),
                            text= 'X'
                elif self.board[i, j] == self.o:
                            font=('Arial', 50),
                            text= 'O'
                            font=('Arial', 50),
                            text= ' '

These methods create the grid which is the gameboard and update values after every move. We delay 0.2 seconds to update the values. The boxes are configured with the fonts, the colors, and the values they should receive.

Get The Result

We define methods to plot the result as a graph and finding the state of the game, if the game is over or not, and the winner.

def plot(x_scores, o_scores):
    plt.xlabel('Number of Games')
    plt.text(len(x_scores)-1, x_scores[-1], str(x_scores[-1]))
    plt.text(len(o_scores)-1, o_scores[-1], str(o_scores[-1]))

def get_state_hash_and_winner(env, i=0, j=0):
    # (i, j) refers to the next box on the board to permute, we need to try -1, 0, 1
    results = []
    for v in [0, env.x, env.o]:
        env.board[i, j] = v  # if board is empty, it should already be 0
        if j == 2:
            if i == 2:
                state = env.get_state()
                gameOver = env.game_over()
                winner = env.winner
                results.append((state, winner, gameOver))
                results += get_state_hash_and_winner(env, i + 1, 0)
            # increment j, i stays the same
            results += get_state_hash_and_winner(env, i, j + 1)
    return results

We plot the graph of the scores of each player. We compare the score of the X-agent with the random moves chosen by the O-player. We view the score with the number of games played by the players.

We loop through the board to see if it is full and if there is a winner or the game is draw. We store the state value, gameover value, and the winner in a list.

Print The Game

A method is used to change the players after each move. Also this method updates the board after each move.

The main function is finally run to call the necessary methods, objects and plotting the graph.

def play_game(x_agent, o_agent, env, print_board):
    current_player = None  # p1 will start the game always
    # loop until the game is over
    continue_game = True
    while continue_game:
        if current_player == x_agent:
            current_player = o_agent
            current_player = x_agent

        # current player makes his move
        # update state histories
        if current_player == x_agent:
            state = env.get_state()
            x_agent.update_state_history(state)  # p1 will be agent
            # update value function for agent
            if print_board:
                env.update_board()  # draw updated board again

        if env.game_over():
            continue_game = False

def main():
    print("Starting the game...")
    x_score = 0
    o_score = 0
    plot_x_scores = []
    plot_o_scores = []

    # initialize empty environment
    env = Game()

    state_winner_triples = get_state_hash_and_winner(env)

    x_agent = X_Agent()
    x_agent.initialize_V(env, state_winner_triples)
    o_agent = O_Agent()
    total_game_played = 0

    while True:
        env = Game()
        play_game(x_agent, o_agent, env=env, print_board=True)

        total_game_played += 1
        if env.winner == env.x:
            x_score += 1
        elif env.winner == env.o:
            o_score += 1
        print("Game: ", total_game_played, " X_Agent score: ", x_score, " O_Agent score: ", o_score)

        plot(plot_x_scores, plot_o_scores)

if __name__ == '__main__':


The gameboard with a draw match

The blue line represents the trained agent X-player score and the orange line represents the O-player score which only chose random moves.


The scores after every game

With this article at OpenGenus, you must have the complete idea of Tic Tac Toe with Reinforcement Learning.

Muhsina Munfa

Muhsina Munfa

Muhsina Munfa is a graduate with a BSc degree in the Department of CSE from Metropolitan University Sylhet.

Read More

Vote for Author of this article:

Improved & Reviewed by:

OpenGenus Foundation OpenGenus Foundation
Tic Tac Toe with Reinforcement Learning
Share this