#Gaming

Tech Blog 14 minutes read (About 2094 words)

In last episode, we finished up minimax strategy for Connect-N games, including Tic-Tac-Toe and Gomoku. This episode, we will implement its GUI environment based on Pygame library for human vs. human, AI vs. AI or human vs. AI plays, which is essential for self-play AlphaGo Zero reinforcement learning. The environment is further embedded into OpenAI Gym as it's the standard in game reinforcement learning. All code in this series is in ConnectNGym github.

Connect-N Pygame Implementation

Pygame Tic-Tac-Toe Human Play

Python has several well-known multi-platform GUI libraries such as Tkinter, PyQt. They are mainly targeted at desktop GUI programming, whose API family is complicated and learning curve is steep. In contrast, Pygame is tailored specifically for desktop small game development so we adopt it. ### Pygame 101

Pygame is, no exceptionally, the same as all GUI development, that is based on single thread event driven model. Here is the simplest desktop Pygame application showing a window. while True infinitely retrieves events dispatched by OS to the window. In the example, we only handle quit event (user clicking on close button) to exit the whole process. In addition, clock variable controls FPS, which we won't elaborate on.

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import sys
import pygame
pygame.init()
display = pygame.display.set_mode((800,600))
clock = pygame.time.Clock()

while True:
	for event in pygame.event.get():
		if event.type == pygame.QUIT:
			sys.exit(0)
		else:
			pygame.display.update()
			clock.tick(1)

PyGameBoard Class

PyGameBoard class encapsulates GUI interaction and rendering logics. In last episode, we have coded ConnectNGame class. PyGameBoard is instantiated with a pre-initialized ConnectNGame instance. It handles GUI mouse event to determine next valid move and then further manipulates its internal state, which is just the ConnectNGame instance passed in. Concretely, PyGameBoard instance method, next_user_input(self) loops until a valid action is identified by current player.

PyGameBoard Class Diagram
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class PyGameBoard:

	def __init__(self, connectNGame: ConnectNGame):
		self.connectNGame = connectNGame
		pygame.init()

	def next_user_input(self) -> Tuple[int, int]:
		self.action = None
		while not self.action:
			self.check_event()
			self._render()
			self.clock.tick(60)
		return self.action
  
  def move(self, r: int, c: int) -> int:
		return self.connectNGame.move(r, c)
  
if __name__ == '__main__':
	connectNGame = ConnectNGame()
	pygameBoard = PyGameBoard(connectNGame)
	while not pygameBoard.isGameOver():
		pos = pygameBoard.next_user_input()
		pygameBoard.move(*pos)

	pygame.quit()

Following Pygame 101, method check_event() handles events dispatched by OS and only player mouse event is consumed. Method _handle_user_input() converts mouse event into row and column indices, validates the move and returns the move in the form of Tuple[int, int]. For instance, (0, 0) is the upper left corner position.

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def check_event(self):
	for e in pygame.event.get():
		if e.type == pygame.QUIT:
			pygame.quit()
			sys.exit(0)
		elif e.type == pygame.MOUSEBUTTONDOWN:
			self._handle_user_input(e)
    
def _handle_user_input(self, e: Event) -> Tuple[int, int]:
	origin_x = self.start_x - self.edge_size
	origin_y = self.start_y - self.edge_size
	size = (self.board_size - 1) * self.grid_size + self.edge_size * 2
	pos = e.pos
	if origin_x <= pos[0] <= origin_x + size and origin_y <= pos[1] <= origin_y + size:
		if not self.connectNGame.gameOver:
			x = pos[0] - origin_x
			y = pos[1] - origin_y
			r = int(y // self.grid_size)
			c = int(x // self.grid_size)
			valid = self.connectNGame.checkAction(r, c)
			if valid:
				self.action = (r, c)
				return self.action

Integrated into OpenAI Gym

OpenAI Gym specifies how Agent interacts with Env. Env is defined as gym.Env and the major task of creating a new game Environment is subclassing it and overriding reset, step and render methods. Let's see how our ConnectNGym looks like.

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class ConnectNGym(gym.Env):

	def reset(self) -> ConnectNGame:
		"""Resets the state of the environment and returns an initial observation.

		Returns:
			observation (object): the initial observation.
		"""
		raise NotImplementedError


	def step(self, action: Tuple[int, int]) -> Tuple[ConnectNGame, int, bool, None]:
		"""Run one timestep of the environment's dynamics. When end of
		episode is reached, you are responsible for calling `reset()`
		to reset this environment's state.

		Accepts an action and returns a tuple (observation, reward, done, info).

		Args:
			action (object): an action provided by the agent

		Returns:
			observation (object): agent's observation of the current environment
			reward (float) : amount of reward returned after previous action
			done (bool): whether the episode has ended, in which case further step() calls will return undefined results
			info (dict): contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)
		"""
		raise NotImplementedError



	def render(self, mode='human'):
		"""
		Renders the environment.

		The set of supported modes varies per environment. (And some
		environments do not support rendering at all.) By convention,
		if mode is:

		- human: render to the current display or terminal and
			return nothing. Usually for human consumption.
		- rgb_array: Return an numpy.ndarray with shape (x, y, 3),
			representing RGB values for an x-by-y pixel image, suitable
			for turning into a video.
		- ansi: Return a string (str) or StringIO.StringIO containing a
			terminal-style text representation. The text can include newlines
			and ANSI escape sequences (e.g. for colors).

		Note:
		Make sure that your class's metadata 'render.modes' key includes
		the list of supported modes. It's recommended to call super()
		in implementations to use the functionality of this method.

		Args:
			mode (str): the mode to render with
		"""
		raise NotImplementedError

Method reset()

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def reset(self) -> ConnectNGame

Resets environment internal state and returns corresponding initial status that can be observed by agent. ConnectNGym holds an instance of ConnectNGame as its internal state and because of the complete observability property in any board games, the observable state by agent is exactly the same as board game internal state. So we return a deepcopy of ConnectNGame instance in reset().

Method step()

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def step(self, action: Tuple[int, int]) -> Tuple[ConnectNGame, int, bool, None]

Once the agent selects an action and hands back to env, env would execute the action and change its internal state via step() and returns following four items.

  1. The new state observed by the agent
  2. The reward associated with the action
  3. Environment terminated or not
  4. Other auxiliary information

step() is the most core API of gym.Env. We illustrate a sequence of game state transitions together with input and output

Initial State:
State ((0, 0, 0), (0, 0, 0), (0, 0, 0))

Agent A selects action = (0, 0). ConnectNGym.step() executes the action and returns

status = ((1, 0, 0), (0, 0, 0), (0, 0, 0))，reward = 0，game_end = False

State ((1, 0, 0), (0, 0, 0), (0, 0, 0))

Agent B selects action = (1, 1)，ConnectNGym.step() executes the action and returns

status = ((1, 0, 0), (0, -1, 0), (0, 0, 0))，reward = 0，game_end = False

State ((1, 0, 0), (0, -1, 0), (0, 0, 0))
Repeat the process and game may end up with following terminal state after 5 rounds.
Terminal State ((1, 1, 1), (-1, -1, 0), (0, 0, 0))

The last step() returns

status = ((1, 1, 1), (-1, -1, 0), (0, 0, 0))，reward = 1，game_end = True

Method render()

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def render(self, mode='human')

Renders the env. Mode parameter differentiates player being human or AI agent.

ConnectNGym Implementation

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class ConnectNGym(gym.Env):

	def __init__(self, pygameBoard: PyGameBoard, isGUI=True, displaySec=2):
		self.pygameBoard = pygameBoard
		self.isGUI = isGUI
		self.displaySec = displaySec
		self.action_space = spaces.Discrete(pygameBoard.board_size * pygameBoard.board_size)
		self.observation_space = spaces.Discrete(pygameBoard.board_size * pygameBoard.board_size)
		self.seed()
		self.reset()

	def reset(self) -> ConnectNGame:
		self.pygameBoard.connectNGame.reset()
		return copy.deepcopy(self.pygameBoard.connectNGame)

	def step(self, action: Tuple[int, int]) -> Tuple[ConnectNGame, int, bool, None]:
		# assert self.action_space.contains(action)

		r, c = action
		reward = REWARD_NONE
		result = self.pygameBoard.move(r, c)
		if self.pygameBoard.isGameOver():
			reward = result

		return copy.deepcopy(self.pygameBoard.connectNGame), reward, not result is None, None

	def render(self, mode='human'):
		if not self.isGUI:
			self.pygameBoard.connectNGame.drawText()
			time.sleep(self.displaySec)
		else:
			self.pygameBoard.display(sec=self.displaySec)

	def get_available_actions(self) -> List[Tuple[int, int]]:
		return self.pygameBoard.getAvailablePositions()

Connect-N Enhanced Minimax Strategy

The following animation shows two minimax AI players playing Tic-Tac-Toe game (k=3,m=n=3). We know the conclusion from previous episode that Tic-Tac-Toe is solved to be a draw, meaning when two players both play optimal strategy, the first player is forced tie by second one, which corresponds to animation result.

Minimax AI Self-Play

Game State Rotation Enhancement

In last episode, we have confirmed Tic-Tac-Toe has 5478 total states. The number grows exponentially as k, m and n increase. For instance, in case where k=3, m=n=4 the total state number is 6035992 whereas k=4, m=n=4 it's 9722011. We could improve Minimax DP strategy by pruning those game states that are rotated from one solved game state. That is, once a game state is solved, we not only cache this game state but also cache other three game states derived by rotation that share the same result.

For example, game state below has same result as other three rotated ones.

Game State 1
Other Rotated 3 States
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def similarStatus(self, status: Tuple[Tuple[int, ...]]) -> List[Tuple[Tuple[int, ...]]]:
	ret = []
	rotatedS = status
	for _ in range(4):
		rotatedS = self.rotate(rotatedS)
		ret.append(rotatedS)
	return ret

def rotate(self, status: Tuple[Tuple[int, ...]]) -> Tuple[Tuple[int, ...]]:
	N = len(status)
	board = [[ConnectNGame.AVAILABLE] * N for _ in range(N)]

	for r in range(N):
		for c in range(N):
			board[c][N - 1 - r] = status[r][c]

	return tuple([tuple(board[i]) for i in range(N)])

Minimax Strategy Precomputation

In last version of Minimax DP strategy implementation, we searched best game result given a game state. In the computation, we also leveraged pruning to shortcut if the result is already best. However, for AI agent, we still have to call minimax for each new game state encountered. This is very inefficient because we are solving same game states again and again during top down recursion. An obvious improvement is to compute all game states in first step and cache them all. Later for each given state encountered, we only need to aggregate result by looking at all possible next move positions of that game state. Code of aggregating possible moves is listed below.

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class PlannedMinimaxStrategy(Strategy):
	def __init__(self, game: ConnectNGame):
		super().__init__()
		self.game = copy.deepcopy(game)
		self.dpMap = {}  # game_status => result, move
		self.result = self.minimax(game.getStatus())


	def action(self, game: ConnectNGame) -> Tuple[int, Tuple[int, int]]:
		game = copy.deepcopy(game)

		player = game.currentPlayer
		bestResult = player * -1  # assume opponent win as worst result
		bestMove = None
		for move in game.getAvailablePositions():
			game.move(*move)
			status = game.getStatus()
			game.undo()

			result = self.dpMap[status]

			if player == ConnectNGame.PLAYER_A:
				bestResult = max(bestResult, result)
			else:
				bestResult = min(bestResult, result)
			# update bestMove if any improvement
			bestMove = move if bestResult == result else bestMove
			print(f'move {move} => {result}')

		return bestResult, bestMove

Agent Class and Playing Logic

We also construct Agent class hierarchy, allowing AI player and human player to share common code.

BaseAgent is the root class with default act() method being making random decisions over all available actions.

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class BaseAgent(object):
	def __init__(self):
		pass

	def act(self, game: PyGameBoard, available_actions):
		return random.choice(available_actions)

AIAgent has its act() behavior delegated to strategy's action() method.

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class AIAgent(BaseAgent):
	def __init__(self, strategy: Strategy):
		self.strategy = strategy

	def act(self, game: PyGameBoard, available_actions):
		result, move = self.strategy.action(game.connectNGame)
		assert move in available_actions
		return move

HumanAgent delegates act() to PyGameBoard next_user_input().

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class HumanAgent(BaseAgent):
	def __init__(self):
		pass

	def act(self, game: PyGameBoard, available_actions):
		return game.next_user_input()
Agent Class Diagram

Below are code snippets showing how Agent, ConnectNGym, PyGameBoard are connected together to make game play.

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def play_ai_vs_ai(env: ConnectNGym):
	plannedMinimaxAgent = AIAgent(PlannedMinimaxStrategy(env.pygameBoard.connectNGame))
	play(env, plannedMinimaxAgent, plannedMinimaxAgent)


def play(env: ConnectNGym, agent1: BaseAgent, agent2: BaseAgent):
	agents = [agent1, agent2]

	while True:
		env.reset()
		done = False
		agent_id = -1
		while not done:
			agent_id = (agent_id + 1) % 2
			available_actions = env.get_available_actions()
			agent = agents[agent_id]
			action = agent.act(pygameBoard, available_actions)
			_, reward, done, info = env.step(action)
			env.render(True)

			if done:
				print(f'result={reward}')
				time.sleep(3)
				break


if __name__ == '__main__':
	pygameBoard = PyGameBoard(connectNGame=ConnectNGame(board_size=3, N=3))
	env = ConnectNGym(pygameBoard)
	env.render(True)

	play_ai_vs_ai(env)
Class Diagram Overview

Tech Blog 23 minutes read (About 3394 words)

This episode extends last one, where Minimax and Alpha Beta Pruning algorithms are introduced. We will solve several tic-tac-toe problems in leetcode, gathering intuition and building blocks for tic-tac-toe game logic, which can be naturally extended to Connect-N game or Gomoku (N=5). Then we solve tic-tac-toe using Minimax and Alpha Beta pruning for small N and analyze their state space. In the following episodes, based on building blocks here, we will implement a Connect-N Open Gym GUI Environment, where we can play against computer visually or compare different computer algorithms. Finally, we demonstrate how to implement a Monte Carlo Tree Search for Connect-N Game.

Leetcode Tic-Tac-Toe Problems

1275. Find Winner on a Tic Tac Toe Game (Easy)

Tic-tac-toe is played by two players A and B on a 3 x 3 grid.
Here are the rules of Tic-Tac-Toe:
Players take turns placing characters into empty squares (" ").
The first player A always places "X" characters, while the second player B always places "O" characters.
"X" and "O" characters are always placed into empty squares, never on filled ones.
The game ends when there are 3 of the same (non-empty) character filling any row, column, or diagonal.
The game also ends if all squares are non-empty.
No more moves can be played if the game is over. Given an array moves where each element is another array of size 2 corresponding to the row and column of the grid where they mark their respective character in the order in which A and B play.
Return the winner of the game if it exists (A or B), in case the game ends in a draw return "Draw", if there are still movements to play return "Pending".
You can assume that moves is valid (It follows the rules of Tic-Tac-Toe), the grid is initially empty and A will play first.

Example 1:
Input: moves = [[0,0],[2,0],[1,1],[2,1],[2,2]]
Output: "A"
Explanation: "A" wins, he always plays first.
"X " "X " "X " "X " "X "
" " -> " " -> " X " -> " X " -> " X "
" " "O " "O " "OO " "OOX"

Example 2:
Input: moves = [[0,0],[1,1],[0,1],[0,2],[1,0],[2,0]]
Output: "B"
Explanation: "B" wins.
"X " "X " "XX " "XXO" "XXO" "XXO"
" " -> " O " -> " O " -> " O " -> "XO " -> "XO "
" " " " " " " " " " "O "

Example 3:
Input: moves = [[0,0],[1,1],[2,0],[1,0],[1,2],[2,1],[0,1],[0,2],[2,2]]
Output: "Draw"
Explanation: The game ends in a draw since there are no moves to make.
"XXO"
"OOX"
"XOX"

Example 4:
Input: moves = [[0,0],[1,1]]
Output: "Pending"
Explanation: The game has not finished yet.
"X "
" O "
" "

The intuitive solution is to permute all 8 possible winning conditions: 3 vertical lines, 3 horizontal lines and 2 diagonal lines. We keep 8 variables representing each winning condition and a simple trick is converting board state to a 3x3 2d array, whose cell has value -1, 1, and 0. In this way, we can traverse the board state exactly once and in the process determine all 8 variables value by summing corresponding cell value. For example, row[0] is for first line winning condition, summed by all 3 cells in first row during board traveral. It indicates win for first player only when it's equal to 3 and win for second player when it's -3.

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# AC
from typing import List

class Solution:
    def tictactoe(self, moves: List[List[int]]) -> str:
        board = [[0] * 3 for _ in range(3)]
        for idx, xy in enumerate(moves):
            player = 1 if idx % 2 == 0 else -1
            board[xy[0]][xy[1]] = player

        turn = 0
        row, col = [0, 0, 0], [0, 0, 0]
        diag1, diag2 = False, False
        for r in range(3):
            for c in range(3):
                turn += board[r][c]
                row[r] += board[r][c]
                col[c] += board[r][c]
                if r == c:
                    diag1 += board[r][c]
                if r + c == 2:
                    diag2 += board[r][c]

        oWin = any(row[r] == 3 for r in range(3)) or any(col[c] == 3 for c in range(3)) or diag1 == 3 or diag2 == 3
        xWin = any(row[r] == -3 for r in range(3)) or any(col[c] == -3 for c in range(3)) or diag1 == -3 or diag2 == -3

        return "A" if oWin else "B" if xWin else "Draw" if len(moves) == 9 else "Pending"

Below we give another AC solution. Despite more code, it's more efficient than previous one because for a given game state, it does not need to visit each cell on the board. How is it achieved? The problem guarentees each move is valid, so what's sufficent to examine is to check neighbours of the final move and see if any line including final move creates a winning condition. Later we will reuse the code in this solution to create tic-tac-toe game logic.

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# AC
from typing import List

class Solution:
    def checkWin(self, r: int, c: int) -> bool:
        north = self.getConnectedNum(r, c, -1, 0)
        south = self.getConnectedNum(r, c, 1, 0)

        east = self.getConnectedNum(r, c, 0, 1)
        west = self.getConnectedNum(r, c, 0, -1)

        south_east = self.getConnectedNum(r, c, 1, 1)
        north_west = self.getConnectedNum(r, c, -1, -1)

        north_east = self.getConnectedNum(r, c, -1, 1)
        south_west = self.getConnectedNum(r, c, 1, -1)

        if (north + south + 1 >= 3) or (east + west + 1 >= 3) or \
                (south_east + north_west + 1 >= 3) or (north_east + south_west + 1 >= 3):
            return True
        return False

    def getConnectedNum(self, r: int, c: int, dr: int, dc: int) -> int:
        player = self.board[r][c]
        result = 0
        i = 1
        while True:
            new_r = r + dr * i
            new_c = c + dc * i
            if 0 <= new_r < 3 and 0 <= new_c < 3:
                if self.board[new_r][new_c] == player:
                    result += 1
                else:
                    break
            else:
                break
            i += 1
        return result

    def tictactoe(self, moves: List[List[int]]) -> str:
        self.board = [[0] * 3 for _ in range(3)]
        for idx, xy in enumerate(moves):
            player = 1 if idx % 2 == 0 else -1
            self.board[xy[0]][xy[1]] = player

        # only check last move
        r, c = moves[-1]
        win = self.checkWin(r, c)
        if win:
            return "A" if len(moves) % 2 == 1 else "B"

        return "Draw" if len(moves) == 9 else "Pending"

Leetcode 794. Valid Tic-Tac-Toe State(Medium)

A Tic-Tac-Toe board is given as a string array board. Return True if and only if it is possible to reach this board position during the course of a valid tic-tac-toe game.
The board is a 3 x 3 array, and consists of characters " ", "X", and "O". The " " character represents an empty square.
Here are the rules of Tic-Tac-Toe:
Players take turns placing characters into empty squares (" ").
The first player A always places "X" characters, while the second player B always places "O" characters.
"X" and "O" characters are always placed into empty squares, never on filled ones.
The game ends when there are 3 of the same (non-empty) character filling any row, column, or diagonal.
The game also ends if all squares are non-empty.
No more moves can be played if the game is over.

Example 1:
Input: board = ["O ", " ", " "]
Output: false
Explanation: The first player always plays "X".

Example 2:
Input: board = ["XOX", " X ", " "]
Output: false
Explanation: Players take turns making moves.

Example 3:
Input: board = ["XXX", " ", "OOO"]
Output: false

Example 4:
Input: board = ["XOX", "O O", "XOX"]
Output: true

Note: board is a length-3 array of strings, where each string board[i] has length 3.
Each board[i][j] is a character in the set {" ", "X", "O"}.

Surely, it can be solved using DFS, checking if the state given would be reached from initial state. However, this involves lots of states to search. Could we do better? There are obvious properties we can rely on. For example, the number of X is either equal to the number of O or one more. If we can enumerate a combination of necessary and sufficient conditions of checking its reachability, we can solve it in O(1) time complexity.

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# AC
from typing import List

class Solution:

    def convertCell(self, c:str):
        return 1 if c == 'X' else -1 if c == 'O' else 0

    def validTicTacToe(self, board: List[str]) -> bool:
        turn = 0
        row, col = [0, 0, 0], [0, 0, 0]
        diag1, diag2 = False, False
        for r in range(3):
            for c in range(3):
                turn += self.convertCell(board[r][c])
                row[r] += self.convertCell(board[r][c])
                col[c] += self.convertCell(board[r][c])
                if r == c:
                    diag1 += self.convertCell(board[r][c])
                if r + c == 2:
                    diag2 += self.convertCell(board[r][c])

        xWin = any(row[r] == 3 for r in range(3)) or any(col[c] == 3 for c in range(3)) or diag1 == 3 or diag2 == 3
        oWin = any(row[r] == -3 for r in range(3)) or any(col[c] == -3 for c in range(3)) or diag1 == -3 or diag2 == -3
        if (xWin and turn == 0) or (oWin and turn == 1):
            return False
        return (turn == 0 or turn == 1) and (not xWin or not oWin)

Leetcode 348. Design Tic-Tac-Toe (Medium, Locked)

Design a Tic-tac-toe game that is played between two players on a n x n grid.
You may assume the following rules:
A move is guaranteed to be valid and is placed on an empty block.
Once a winning condition is reached, no more moves is allowed.
A player who succeeds in placing n of their marks in a horizontal, vertical, or diagonal row wins the game.

Example:
Given n = 3, assume that player 1 is "X" and player 2 is "O" in the board.
TicTacToe toe = new TicTacToe(3);

toe.move(0, 0, 1); -> Returns 0 (no one wins)
|X| | |
| | | | // Player 1 makes a move at (0, 0).
| | | |

toe.move(0, 2, 2); -> Returns 0 (no one wins)
|X| |O|
| | | | // Player 2 makes a move at (0, 2).
| | | |

toe.move(2, 2, 1); -> Returns 0 (no one wins)
|X| |O|
| | | | // Player 1 makes a move at (2, 2).
| | |X|

toe.move(1, 1, 2); -> Returns 0 (no one wins)
|X| |O|
| |O| | // Player 2 makes a move at (1, 1).
| | |X|

toe.move(2, 0, 1); -> Returns 0 (no one wins)
|X| |O|
| |O| | // Player 1 makes a move at (2, 0).
|X| |X|

toe.move(1, 0, 2); -> Returns 0 (no one wins)
|X| |O|
|O|O| | // Player 2 makes a move at (1, 0).
|X| |X|

toe.move(2, 1, 1); -> Returns 1 (player 1 wins)
|X| |O|
|O|O| | // Player 1 makes a move at (2, 1).
|X|X|X|

Follow up:
Could you do better than O(n2) per move() operation?

348 is a locked problem. For each player's move, we can resort to checkWin function in second solution for 1275. We show another solution based on first solution of 1275, where 8 winning condition flags are kept and each move only touches associated several flag variables.

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# AC
class TicTacToe:

    def __init__(self, n:int):
        """
        Initialize your data structure here.
        :type n: int
        """
        self.row, self.col, self.diag1, self.diag2, self.n = [0] * n, [0] * n, 0, 0, n

    def move(self, row:int, col:int, player:int) -> int:
        """
        Player {player} makes a move at ({row}, {col}).
        @param row The row of the board.
        @param col The column of the board.
        @param player The player, can be either 1 or 2.
        @return The current winning condition, can be either:
                0: No one wins.
                1: Player 1 wins.
                2: Player 2 wins.
        """
        if player == 2:
            player = -1

        self.row[row] += player
        self.col[col] += player
        if row == col:
            self.diag1 += player
        if row + col == self.n - 1:
            self.diag2 += player

        if self.n in [self.row[row], self.col[col], self.diag1, self.diag2]:
            return 1
        if -self.n in [self.row[row], self.col[col], self.diag1, self.diag2]:
            return 2
        return 0


Optimal Strategy of Tic-Tac-Toe

Tic-tac-toe and Gomoku (Connect Five in a Row) share the same rules and are generally considered as M,n,k-game, where board size range to M x N and winning condition changes to k.

ConnectNGame class implements M,n,k-game of MxM board size. It encapsulates the logic of checking each move and also is able to undo last move to facilitate backtrack in game search algorithm later.

ConnectNGame

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class ConnectNGame:

    PLAYER_A = 1
    PLAYER_B = -1
    AVAILABLE = 0
    RESULT_TIE = 0
    RESULT_A_WIN = 1
    RESULT_B_WIN = -1

    def __init__(self, N:int = 3, board_size:int = 3):
        assert N <= board_size
        self.N = N
        self.board_size = board_size
        self.board = [[ConnectNGame.AVAILABLE] * board_size for _ in range(board_size)]
        self.gameOver = False
        self.gameResult = None
        self.currentPlayer = ConnectNGame.PLAYER_A
        self.remainingPosNum = board_size * board_size
        self.actionStack = []

    def move(self, r: int, c: int) -> int:
        """

        :param r:
        :param c:
        :return: None: game ongoing
        """
        assert self.board[r][c] == ConnectNGame.AVAILABLE
        self.board[r][c] = self.currentPlayer
        self.actionStack.append((r, c))
        self.remainingPosNum -= 1
        if self.checkWin(r, c):
            self.gameOver = True
            self.gameResult = self.currentPlayer
            return self.currentPlayer
        if self.remainingPosNum == 0:
            self.gameOver = True
            self.gameResult = ConnectNGame.RESULT_TIE
            return ConnectNGame.RESULT_TIE
        self.currentPlayer *= -1

    def undo(self):
        if len(self.actionStack) > 0:
            lastAction = self.actionStack.pop()
            r, c = lastAction
            self.board[r][c] = ConnectNGame.AVAILABLE
            self.currentPlayer = ConnectNGame.PLAYER_A if len(self.actionStack) % 2 == 0 else ConnectNGame.PLAYER_B
            self.remainingPosNum += 1
            self.gameOver = False
            self.gameResult = None
        else:
            raise Exception('No lastAction')

    def getAvailablePositions(self) -> List[Tuple[int, int]]:
        return [(i,j) for i in range(self.board_size) for j in range(self.board_size) if self.board[i][j] == ConnectNGame.AVAILABLE]

    def getStatus(self) -> Tuple[Tuple[int, ...]]:
        return tuple([tuple(self.board[i]) for i in range(self.board_size)])

Note that checkWin code is identical to second solution in 1275.

Minimax Strategy

Now we have Connect-N game logic, let's finish its minimax algorithm to solve the game.

Define a generic strategy base class, where action method needs to be overridden. Action method expects ConnectNGame class telling current game state and returns a tuple of 2 elements, the first element is the estimated or exact game result after taking action specified by second element. The second element is of form Tuple[int, int], denoting the position of the move, for instance, (1，1).

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class Strategy(ABC):

    def __init__(self):
        super().__init__()

    @abstractmethod
    def action(self, game: ConnectNGame) -> Tuple[int, Tuple[int, int]]:
        pass
MinimaxStrategy code is very similar to previous minimax algorithms. The only added piece is the corresponding move returned by action method.
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class MinimaxStrategy(Strategy):
    def action(self, game: ConnectNGame) -> Tuple[int, Tuple[int, int]]:
        self.game = copy.deepcopy(game)
        result, move = self.minimax()
        return result, move

    def minimax(self) -> Tuple[int, Tuple[int, int]]:
        game = self.game
        bestMove = None
        assert not game.gameOver
        if game.currentPlayer == ConnectNGame.PLAYER_A:
            ret = -math.inf
            for pos in game.getAvailablePositions():
                move = pos
                result = game.move(*pos)
                if result is None:
                    assert not game.gameOver
                    result, oppMove = self.minimax()
                game.undo()
                ret = max(ret, result)
                bestMove = move if ret == result else bestMove
                if ret == 1:
                    return 1, move
            return ret, bestMove
        else:
            ret = math.inf
            for pos in game.getAvailablePositions():
                move = pos
                result = game.move(*pos)
                if result is None:
                    assert not game.gameOver
                    result, oppMove = self.minimax()
                game.undo()
                ret = min(ret, result)
                bestMove = move if ret == result else bestMove
                if ret == -1:
                    return -1, move
            return ret, bestMove
We plot up to first 2 moves with code above. For first player O, there are possibly 9 positions, where due to symmetry, only 3 kinds of moves, which we call corner, edge and center, respectively. The following graph shows whatever 9 positions the first player takes, the best result is draw. So solution of tic-tac-toe is draw.
Tic-tac-toe 9 First Step
Plot first step of 3 kinds of moves one by one below.
Tic-tac-toe First Step Corner
Tic-tac-toe First Step Edge
Tic-tac-toe First Step Center

Tic-tac-toe Solution and Number of States

An interesting question is the number of game states of tic-tac-toe. A loosely upper bound can be derived by \(3^9=19683\), which includes lots of inreachable states. This article Tic-Tac-Toe (Naughts and Crosses, Cheese and Crackers, etc lists number of states after each move. The total number is 5478.

Moves Positions Terminal Positions
0 1
1 9
2 72
3 252
4 756
5 1260 120
6 1520 148
7 1140 444
8 390 168
9 78 78
Total 5478 958

We can verify the number if we change a little of existing code to code below.

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class CountingMinimaxStrategy(Strategy):
    def action(self, game: ConnectNGame) -> Tuple[int, Tuple[int, int]]:
        self.game = copy.deepcopy(game)
        self.dpMap = {}
        result, move = self.minimax(game.getStatus())
        return result, move

    def minimax(self, gameStatus: Tuple[Tuple[int, ...]]) -> Tuple[int, Tuple[int, int]]:
        # print(f'Current {len(strategy.dpMap)}')

        if gameStatus in self.dpMap:
            return self.dpMap[gameStatus]

        game = self.game
        bestMove = None
        assert not game.gameOver
        if game.currentPlayer == ConnectNGame.PLAYER_A:
            ret = -math.inf
            for pos in game.getAvailablePositions():
                move = pos
                result = game.move(*pos)
                if result is None:
                    assert not game.gameOver
                    result, oppMove = self.minimax(game.getStatus())
                    self.dpMap[game.getStatus()] = result, oppMove
                else:
                    self.dpMap[game.getStatus()] = result, move
                game.undo()
                ret = max(ret, result)
                bestMove = move if ret == result else bestMove
            self.dpMap[gameStatus] = ret, bestMove
            return ret, bestMove
        else:
            ret = math.inf
            for pos in game.getAvailablePositions():
                move = pos
                result = game.move(*pos)

                if result is None:
                    assert not game.gameOver
                    result, oppMove = self.minimax(game.getStatus())
                    self.dpMap[game.getStatus()] = result, oppMove
                else:
                    self.dpMap[game.getStatus()] = result, move
                game.undo()
                ret = min(ret, result)
                bestMove = move if ret == result else bestMove
            self.dpMap[gameStatus] = ret, bestMove
            return ret, bestMove


if __name__ == '__main__':
    tic_tac_toe = ConnectNGame(N=3, board_size=3)
    strategy = CountingMinimaxStrategy()
    strategy.action(tic_tac_toe)
    print(f'Game States Number {len(strategy.dpMap)}')

Running the code proves the total number is 5478. Also illustrate some small scale game configuration results.

3x3 4x4
k=3 5478 （Draw) 6035992 （Win）
k=4 9722011 （Draw）
k=5

According to Wikipedia M,n,k-game, below are results for some game configuration.

3x3 4x4 5x5 6x6
k=3 Draw Win Win Win
k=4 Draw Draw Win
k=5 Draw Draw

What's worth mentioning is that Gomoku (Connect Five in a Row), of board size MxM >= 15x15 is proved by L. Victor Allis to be Win.

Alpha-Beta Pruning Strategy

Alpha Beta Pruning Strategy is pasted below.

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class AlphaBetaStrategy(Strategy):
    def action(self, game: ConnectNGame) -> Tuple[int, Tuple[int, int]]:
        self.game = game
        result, move = self.alpha_beta(self.game.getStatus(), -math.inf, math.inf)
        return result, move

    def alpha_beta(self, gameStatus: Tuple[Tuple[int, ...]], alpha:int=None, beta:int=None) -> Tuple[int, Tuple[int, int]]:
        game = self.game
        bestMove = None
        assert not game.gameOver
        if game.currentPlayer == ConnectNGame.PLAYER_A:
            ret = -math.inf
            for pos in game.getAvailablePositions():
                move = pos
                result = game.move(*pos)
                if result is None:
                    assert not game.gameOver
                    result, oppMove = self.alpha_beta(game.getStatus(), alpha, beta)
                game.undo()
                alpha = max(alpha, result)
                ret = max(ret, result)
                bestMove = move if ret == result else bestMove
                if alpha >= beta or ret == 1:
                    return ret, move
            return ret, bestMove
        else:
            ret = math.inf
            for pos in game.getAvailablePositions():
                move = pos
                result = game.move(*pos)
                if result is None:
                    assert not game.gameOver
                    result, oppMove = self.alpha_beta(game.getStatus(), alpha, beta)
                game.undo()
                beta = min(beta, result)
                ret = min(ret, result)
                bestMove = move if ret == result else bestMove
                if alpha >= beta or ret == -1:
                    return ret, move
            return ret, bestMove

Rewrite alpha beta pruning with DP, where we omit alpha and beta parameters in alpha_beta_dp because lru_cache cannot specify effective parameters. Instead, we keep alpha and beta in a stack variable and maintain the stack according to alpha_bate_dp calling stack.

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class AlphaBetaDPStrategy(Strategy):
    def action(self, game: ConnectNGame) -> Tuple[int, Tuple[int, int]]:
        self.game = game
        self.alphaBetaStack = [(-math.inf, math.inf)]
        result, move = self.alpha_beta_dp(self.game.getStatus())
        return result, move

    @lru_cache(maxsize=None)
    def alpha_beta_dp(self, gameStatus: Tuple[Tuple[int, ...]]) -> Tuple[int, Tuple[int, int]]:
        alpha, beta = self.alphaBetaStack[-1]
        game = self.game
        bestMove = None
        assert not game.gameOver
        if game.currentPlayer == ConnectNGame.PLAYER_A:
            ret = -math.inf
            for pos in game.getAvailablePositions():
                move = pos
                result = game.move(*pos)
                if result is None:
                    assert not game.gameOver
                    self.alphaBetaStack.append((alpha, beta))
                    result, oppMove = self.alpha_beta_dp(game.getStatus())
                    self.alphaBetaStack.pop()
                game.undo()
                alpha = max(alpha, result)
                ret = max(ret, result)
                bestMove = move if ret == result else bestMove
                if alpha >= beta or ret == 1:
                    return ret, move
            return ret, bestMove
        else:
            ret = math.inf
            for pos in game.getAvailablePositions():
                move = pos
                result = game.move(*pos)
                if result is None:
                    assert not game.gameOver
                    self.alphaBetaStack.append((alpha, beta))
                    result, oppMove = self.alpha_beta_dp(game.getStatus())
                    self.alphaBetaStack.pop()
                game.undo()
                beta = min(beta, result)
                ret = min(ret, result)
                bestMove = move if ret == result else bestMove
                if alpha >= beta or ret == -1:
                    return ret, move
            return ret, bestMove

Tech Blog 22 minutes read (About 3281 words)

This series, we deal with zero-sum turn-based board game algorithm, a sub type of combinatorial games. We start off with small search space problem, introduce classic algorithms and corresponding combinatorial gaming theory and ultimately end with modern approximating Deep RL techniques. From there, after stepping stone is laid, we are able to learn and appreciate how AlphaGo works. In this first episode, we illustrate 3 classic gaming problems in leetcode and solve them from brute force version to DP version then finally rewrite them using classic gaming algorithms, minimax and alpha beta pruning.

Leetcode 292 Nim Game (Easy)

Let's start with an easy Leetcode gaming problem, Leetcode 292 Nim Game.

You are playing the following Nim Game with your friend: There is a heap of stones on the table, each time one of you take turns to remove 1 to 3 stones. The one who removes the last stone will be the winner. You will take the first turn to remove the stones.
Both of you are very clever and have optimal strategies for the game. Write a function to determine whether you can win the game given the number of stones in the heap.

Example:
Input: 4
Output: false
Explanation: If there are 4 stones in the heap, then you will never win the game;
No matter 1, 2, or 3 stones you remove, the last stone will always be removed by your friend.

Let \(f(n)\) be the result, either Win or Lose, when you take turn to make optimal move for the case of \(n\) stones. The first non trial case is \(f(4)\). By playing optimal strategies, it is equivalent to saying if there is any chance that leads to Win, you will definitely choose it. So you try 1, 2, 3 stones and see whether your opponent has any chance to win. Obviously, \(f(1) = f(2) = f(3) = Win\). Therefore, \(f(4)\) is guranteed to lose. Generally, the recurrence relation is given by \[ f(n) = \neg (f(n-1) \land f(n-2) \land f(n-3)) \]

This translates straightforwardly to following Python 3 code.
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# TLE
# Time Complexity: O(exponential)
class Solution_BruteForce:

    def canWinNim(self, n: int) -> bool:
        if n <= 3:
            return True
        for i in range(1, 4):
            if not self.canWinNim(n - i):
                return True
        return False
Since this brute force version has same recursive manner as fibonacci number, the complexity is exponential so it won't pass test. This can be visually verified by following call graph. Notice, node 5 is expanded entirely twice and node 4 is expanded 4 times.
292 Nim Game Brute Force Call Graph, n=7
As what we optimize for computing fibonacci, we cache the result for smaller number and compute larger value based on previous ones. In Python, we can achieve the DP cache effect by merely adding one line, the magical decorator lru_cache. In this way, runtime complexity is drastically reduced to \(O(N)\).
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# RecursionError: maximum recursion depth exceeded in comparison n=1348820612
# Time Complexity: O(N)
class Solution_DP:
    from functools import lru_cache
    @lru_cache(maxsize=None)
    def canWinNim(self, n: int) -> bool:
        if n <= 3:
            return True
        for i in range(1, 4):
            if not self.canWinNim(n - i):
                return True
        return False
Plotting the call graph below helps to verify that. This time, node 5 and 4 are not explored to bottom multiple times. The green node denotes such cache hit.
292 Nim Game DP Call Graph, n=7

However, for this problem, lru_cache is not enough to AC because for large n, such as 1348820612, the implementation suffers from stack overflow. We can, of course, rewrite it in iterative forwarding loop manner. But still TLE.

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# TLE for 1348820612
# Time Complexity: O(N)
class Solution:
    def canWinNim(self, n: int) -> bool:
        if n <= 3:
            return True
        last3, last2, last1 = True, True, True
        for i in range(4, n+1):
            this = not (last3 and last2 and last1)
            last3, last2, last1 = last2, last1, this
        return last1

So AC code requires at most sublinear complexity. The last version also gives us some intuition that win lose may have period of 4. Actually, if you arrange all \(f(n)\) one by one, it's obvious that any \(n \mod 4 = 0\) leads to Lose and other cases lead to Win. Why? Suppose you start with \(4k+i (i=1,2,3)\), you can always remove \(i\) stones and leave \(4k\) stones to your opponent. Whatever he chooses, you are returned with situation \(4k_1 + i_1 (i_1 = 1,2,3)\). This pattern repeats until you have 1, 2, 3 remaining stones.

Win Lose Distribution

Below is one liner AC version.

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# AC
# Time Complexity: O(1)
class Solution:
    def canWinNim(self, n: int) -> bool:
        return not (n % 4 == 0)

Leetcode 486 Predict the Winner (Medium)

Let's exercise a harder problem, Leetcode 486 Predict the Winner.

Given an array of scores that are non-negative integers. Player 1 picks one of the numbers from either end of the array followed by the player 2 and then player 1 and so on. Each time a player picks a number, that number will not be available for the next player. This continues until all the scores have been chosen. The player with the maximum score wins.
Given an array of scores, predict whether player 1 is the winner. You can assume each player plays to maximize his score.

Example 1:
Input: [1, 5, 2]
Output: False
Explanation: Initially, player 1 can choose between 1 and 2.
If he chooses 2 (or 1), then player 2 can choose from 1 (or 2) and 5. If player 2 chooses 5, then player 1 will be left with 1 (or 2).
So, final score of player 1 is 1 + 2 = 3, and player 2 is 5.
Hence, player 1 will never be the winner and you need to return False.

Example 2:
Input: [1, 5, 233, 7]
Output: True
Explanation: Player 1 first chooses 1. Then player 2 have to choose between 5 and 7. No matter which number player 2 choose, player 1 can choose 233.
Finally, player 1 has more score (234) than player 2 (12), so you need to return True representing player1 can win.

For a player, he can choose leftmost or rightmost one and leave remaining array to his opponent. Let us define maxDiff(l, r) to be the maximum difference current player can get, who is facing situation of subarray \([l, r]\).

\[ \begin{equation*} \operatorname{maxDiff}(l, r) = \max \begin{cases} nums[l] - \operatorname{maxDiff}(l + 1, r)\\\\ nums[r] - \operatorname{maxDiff}(l, r - 1) \end{cases} \end{equation*} \]

Runtime complexity can be written as following recurrence. \[ f(n) = 2f(n-1) = O(2^n) \]

Surprisingly, this time brute force version passed, but on the edge of rejection (6300ms).

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# AC
# Time Complexity: O(2^N)
# Slow: 6300ms
from typing import List

class Solution:

    def maxDiff(self, l: int, r:int) -> int:
        if l == r:
            return self.nums[l]
        return max(self.nums[l] - self.maxDiff(l + 1, r), self.nums[r] - self.maxDiff(l, r - 1))

    def PredictTheWinner(self, nums: List[int]) -> bool:
        self.nums = nums
        return self.maxDiff(0, len(nums) - 1) >= 0

Exponential runtime complexity can also be verified by call graph below.
486 Predict the Winner Brute Force Call Graph, n=4

Again, be aware we have repeated computation over same node, for example, [1-2] node is expanded entirely for the second time when going from root to right node. Applying the same lru_cache trick, the one liner decorating maxDiff, we passed again with runtime complexity \(O(n^2)\) and running time 43ms, trial change but substantial improvement!

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# AC
# Time Complexity: O(N^2)
# Fast: 43ms
from functools import lru_cache
from typing import List

class Solution:

    @lru_cache(maxsize=None)
    def maxDiff(self, l: int, r:int) -> int:
        if l == r:
            return self.nums[l]
        return max(self.nums[l] - self.maxDiff(l + 1, r), self.nums[r] - self.maxDiff(l, r - 1))

    def PredictTheWinner(self, nums: List[int]) -> bool:
        self.nums = nums
        return self.maxDiff(0, len(nums) - 1) >= 0

Taking look at DP version call graph, this time, [1-2] node is not re-computed in right branch.
486 Predict the Winner DP Call Graph, n=4

Leetcode 464 Can I Win (Medium)

A similar but slightly difficult problem is Leetcode 464 Can I Win, where bit mask with DP technique is employed.

In the "100 game," two players take turns adding, to a running total, any integer from 1..10. The player who first causes the running total to reach or exceed 100 wins.
What if we change the game so that players cannot re-use integers?
For example, two players might take turns drawing from a common pool of numbers of 1..15 without replacement until they reach a total >= 100.
Given an integer maxChoosableInteger and another integer desiredTotal, determine if the first player to move can force a win, assuming both players play optimally.
You can always assume that maxChoosableInteger will not be larger than 20 and desiredTotal will not be larger than 300.

Example
Input:
maxChoosableInteger = 10
desiredTotal = 11
Output:
false
Explanation:
No matter which integer the first player choose, the first player will lose.
The first player can choose an integer from 1 up to 10.
If the first player choose 1, the second player can only choose integers from 2 up to 10.
The second player will win by choosing 10 and get a total = 11, which is >= desiredTotal.
Same with other integers chosen by the first player, the second player will always win.

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# AC
# Time Complexity: O:(2^m*m), m: maxChoosableInteger
class Solution:
    from functools import lru_cache
    @lru_cache(maxsize=None)
    def recurse(self, status: int, currentTotal: int) -> bool:
        for i in range(1, self.maxChoosableInteger + 1):
            if not (status >> i & 1):
                new_status = 1 << i | status
                if currentTotal + i >= self.desiredTotal:
                    return True
                if not self.recurse(new_status, currentTotal + i):
                    return True
        return False


    def canIWin(self, maxChoosableInteger: int, desiredTotal: int) -> bool:
        self.maxChoosableInteger = maxChoosableInteger
        self.desiredTotal = desiredTotal

        sum = maxChoosableInteger * (maxChoosableInteger + 1) / 2
        if sum < desiredTotal:
            return False
        return self.recurse(0, 0)

Because there are \(2^m\) states and for each state we need to probe at most \(m\) options, so the overall runtime complexity is \(O(m 2^m)\), where m is maxChoosableInteger.

Minimax Algorithm

Up till now, we've seen serveral zero-sum turn based gaming in leetcode. In fact, there is more general algorithm for this type of gaming, named, minimax algorithm with alternate moves. The general setting is that, two players play in turn. The first player is trying to maximize game value and second player trying to minimize game value. For example, the following graph shows all nodes, labelled by its value. Computing from bottom up, the first player (max) can get optimal value -7, assuming both players play optimially.

Wikipedia Minimax Example

Pseudo code in Python 3 is listed below.

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def minimax(node: Node, depth: int, maximizingPlayer: bool) -> int:
    if depth == 0 or is_terminal(node):
        return evaluate_terminal(node)
    if maximizingPlayer:
        value:int = −∞
        for child in node:
            value = max(value, minimax(child, depth − 1, False))
        return value
    else: # minimizing player
        value := +∞
        for child in node:
            value = min(value, minimax(child, depth − 1, True))
        return value

Minimax: 486 Predict the Winner

We know leetcode 486 Predict the Winner is zero-sum turn-based game. Hence, theoretically, we can come up with a minimax algorithm for it. But the difficulty lies in how we define value or utility for it. In previous section, we've defined maxDiff(l, r) to be the maximum difference for current player, who is left with sub array \([l, r]\). In the most basic case, where only one element x is left, it's intuitive to define +x for max player and -x for min player. If we merge it with minimax algorithm, it's naturally follows that, the total reward got by max player is \(+a_1 + a_2 + ... = A\) and reward by min player is \(-b_1 - b_2 - ... = -B\), and max player aims to \(max(A-B)\) while min player aims to \(min(A-B)\). With that in mind, code is not hard to implement.

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# AC
from functools import lru_cache
from typing import List

class Solution:
    # max_player: max(A - B)
    # min_player: min(A - B)
    @lru_cache(maxsize=None)
    def minimax(self, l: int, r: int, isMaxPlayer: bool) -> int:
        if l == r:
            return self.nums[l] * (1 if isMaxPlayer else -1)

        if isMaxPlayer:
            return max(
                self.nums[l] + self.minimax(l + 1, r, not isMaxPlayer),
                self.nums[r] + self.minimax(l, r - 1, not isMaxPlayer))
        else:
            return min(
                -self.nums[l] + self.minimax(l + 1, r, not isMaxPlayer),
                -self.nums[r] + self.minimax(l, r - 1, not isMaxPlayer))

    def PredictTheWinner(self, nums: List[int]) -> bool:
        self.nums = nums
        v = self.minimax(0, len(nums) - 1, True)
        return v >= 0
Minimax 486 Case [1, 5, 2, 4]

Minimax: 464 Can I Win

For this problem, as often processed in other win-lose-tie game without intermediate intrinsic value, it's typically to define +1 in case max player wins, -1 for min player and 0 for tie. Note the shortcut case for both player. For example, the max player can report Win (value=1) once he finds winning condition (>=desiredTotal) is satisfied during enumerating possible moves he can make. This also makes sense since if he gets 1 during maxing, there can not be other value for further probing that is finally returned. The same optimization will be generalized in the next improved algorithm, alpha beta pruning.

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# AC
class Solution:
    from functools import lru_cache
    @lru_cache(maxsize=None)
    # currentTotal < desiredTotal
    def minimax(self, status: int, currentTotal: int, isMaxPlayer: bool) -> int:
        import math
        if status == self.allUsed:
            return 0  # draw: no winner

        if isMaxPlayer:
            value = -math.inf
            for i in range(1, self.maxChoosableInteger + 1):
                if not (status >> i & 1):
                    new_status = 1 << i | status
                    if currentTotal + i >= self.desiredTotal:
                        return 1  # shortcut
                    value = max(value, self.minimax(new_status, currentTotal + i, not isMaxPlayer))
                    if value == 1:
                        return 1
            return value
        else:
            value = math.inf
            for i in range(1, self.maxChoosableInteger + 1):
                if not (status >> i & 1):
                    new_status = 1 << i | status
                    if currentTotal + i >= self.desiredTotal:
                        return -1  # shortcut
                    value = min(value, self.minimax(new_status, currentTotal + i, not isMaxPlayer))
                    if value == -1:
                        return -1
            return value

Alpha-Beta Pruning

We sensed there is space of optimaization during searching, as illustrated in 464 Can I Win minimax algorithm. Let's formalize this idea, called alpha beta pruning. For each node, we maintain two values alpha and beta, which represent the minimum score that the maximizing player is assured of and the maximum score that the minimizing player is assured of, respectively. The root node has initial alpha = −∞ and beta = +∞, forming valid duration [−∞, +∞]. During top down traversal, child node inherits alpha beta value from its parent node, for example, [alpha, beta], if the updated alpha or beta in the child node no longer forms a valid interval, the branch can be pruned and return immediately. Take following example in Wikimedia for example.

  1. Root node, intially: alpha = −∞, beta = +∞

  2. Root node, after 4 is returned, alpha = 4, beta = +∞

  3. Root node, after 5 is returned, alpha = 5, beta = +∞

  4. Rightmost Min node, intially: alpha = 5, beta = +∞

  5. Rightmost Min node, after 1 is returned: alpha = 5, beta = 1

Here we see [5, 1] no longer is valid interval, so it returns without further probing his 2nd and 3rd child. Why? because if the other child returns value > 1, say 2, it will be replaced by 1 as it's a min node with guarenteed value 1. If the other child returns value < 1, it will be abandoned by root node, a max node, which has already guarenteed to have value >=5. So in this situation, whatever other children return does not impact anything.

Wikimedia Alpha Beta Pruning Example

Pseudo code in Python 3 is listed below.

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def alpha_beta(node: Node, depth: int, α: int, β: int, maximizingPlayer: bool) -> int:
    if depth == 0 or is_terminal(node):
        return evaluate_terminal(node)
    if maximizingPlayer:
        value: int = −∞
        for child in node:
            value = max(value, alphabeta(child, depth − 1, α, β, False))
            α = max(α, value)
            if α >= β:
                break # β cut-off
        return value
    else:
        value: int = +∞
        for child in node:
            value = min(value, alphabeta(child, depth − 1, α, β, True))
            β = min(β, value)
            if β <= α:
                break # α cut-off
        return value

Alpha-Beta Pruning: 486 Predict the Winner

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# AC
import math
from functools import lru_cache
from typing import List

class Solution:
    def alpha_beta(self, l: int, r: int, curr: int, isMaxPlayer: bool, alpha: int, beta: int) -> int:
        if l == r:
            return curr + self.nums[l] * (1 if isMaxPlayer else -1)

        if isMaxPlayer:
            ret = self.alpha_beta(l + 1, r, curr + self.nums[l], not isMaxPlayer, alpha, beta)
            alpha = max(alpha, ret)
            if alpha >= beta:
                return alpha
            ret = max(ret, self.alpha_beta(l, r - 1, curr + self.nums[r], not isMaxPlayer, alpha, beta))
            return ret
        else:
            ret = self.alpha_beta(l + 1, r, curr - self.nums[l], not isMaxPlayer, alpha, beta)
            beta = min(beta, ret)
            if alpha >= beta:
                return beta
            ret = min(ret, self.alpha_beta(l, r - 1, curr - self.nums[r], not isMaxPlayer, alpha, beta))
            return ret

    def PredictTheWinner(self, nums: List[int]) -> bool:
        self.nums = nums
        v = self.alpha_beta(0, len(nums) - 1, 0, True, -math.inf, math.inf)
        return v >= 0

Alpha-Beta Pruning: 464 Can I Win

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# AC
class Solution:
    from functools import lru_cache
    @lru_cache(maxsize=None)
    # currentTotal < desiredTotal
    def alpha_beta(self, status: int, currentTotal: int, isMaxPlayer: bool, alpha: int, beta: int) -> int:
        import math
        if status == self.allUsed:
            return 0  # draw: no winner

        if isMaxPlayer:
            value = -math.inf
            for i in range(1, self.maxChoosableInteger + 1):
                if not (status >> i & 1):
                    new_status = 1 << i | status
                    if currentTotal + i >= self.desiredTotal:
                        return 1  # shortcut
                    value = max(value, self.alpha_beta(new_status, currentTotal + i, not isMaxPlayer, alpha, beta))
                    alpha = max(alpha, value)
                    if alpha >= beta:
                        return value
            return value
        else:
            value = math.inf
            for i in range(1, self.maxChoosableInteger + 1):
                if not (status >> i & 1):
                    new_status = 1 << i | status
                    if currentTotal + i >= self.desiredTotal:
                        return -1  # shortcut
                    value = min(value, self.alpha_beta(new_status, currentTotal + i, not isMaxPlayer, alpha, beta))
                    beta = min(beta, value)
                    if alpha >= beta:
                        return value
            return value

C++, Java, Javascript for 486 Predict the Winner

As a bonus, we AC leetcode 486 in C++, Java and Javascript with a bottom up iterative DP. We illustrate this method for other languages not just because lru_cache is available in non Python languages, but also because there are other ways to solve the problem. Notice the topological ordering of DP dependency, building larger DP based on smaller and solved ones. In addition, it's worth mentioning that this approach is guaranteed to have \(n^2\) loops but top down caching approach can have sub \(n^2\) loops.

Java AC Code

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// AC
class Solution {
    public boolean PredictTheWinner(int[] nums) {
        int n = nums.length;
        int[][] dp = new int[n][n];
        for (int i = 0; i < n; i++) {
            dp[i][i] = nums[i];
        }

        for (int l = n - 1; l >= 0; l--) {
            for (int r = l + 1; r < n; r++) {
                dp[l][r] = Math.max(
                        nums[l] - dp[l + 1][r],
                        nums[r] - dp[l][r - 1]);
            }
        }
        return dp[0][n - 1] >= 0;
    }
}

C++ AC Code

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// AC
class Solution {
public:
    bool PredictTheWinner(vector<int>& nums) {
        int n = nums.size();
        vector<vector<int>> dp(n, vector<int>(n, 0));
        for (int i = 0; i < n; i++) {
          dp[i][i] = nums[i];
        }
        for (int l = n - 1; l >= 0; l--) {
            for (int r = l + 1; r < n; r++) {
                dp[l][r] = max(nums[l] - dp[l + 1][r], nums[r] - dp[l][r - 1]);
            }
        }
        return dp[0][n - 1] >= 0;
    }
};

Javascript AC Code

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/**
 * @param {number[]} nums
 * @return {boolean}
 */
var PredictTheWinner = function(nums) {
    const n = nums.length;
    const dp = new Array(n).fill().map(() => new Array(n));

    for (let i = 0; i < n; i++) {
      dp[i][i] = nums[i];
    }
  
    for (let l = n - 1; l >=0; l--) {
        for (let r = i + 1; r < n; r++) {
            dp[l][r] = Math.max(nums[l] - dp[l + 1][r],nums[r] - dp[l][r - 1]);
        }
    }
  
    return dp[0][n-1] >=0;
};
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