In this task you will create an agent to intelligently play the 2048-puzzle game, using more advanced techniques to probe the search space than the simple methods used in the previous assignment. If you have not played the game before, you may do so at gabrielecirulli.github.io/2048 to get a sense of how the game works. You will implement an adversarial search algorithm that plays the game intelligently, perhaps much more so than playing by hand.
Please read all sections of the instructions carefully.
I. Introduction II. Algorithm Review III. Using The Skeleton Code IV. What You Need To Submit V. Important Information
VI. Before You Submit
I. Introduction
An instance of the 2048-puzzle game is played on a 4×4 grid, with numbered tiles that slide in all four directions when a player moves them. Every turn, a new tile will randomly appear in an empty spot on the board, with a value of either 2 or 4. Per the input direction given by the player, all tiles on the grid slide as far as possible in that direction, until they either (1) collide with another tile, or (2) collide with the edge of the grid. If two tiles of the same number collide while moving, they will merge into a single tile, valued at the sum of the two original tiles that collided. The resulting tile cannot merge with another tile again in the same move.
In the first assignment, you had ample experience with the process of abstracting ideas and designing functions, classes, and data structures. The goal was to get familiar with how objects, states, nodes, functions, and implicit or explicit search trees are implemented and interact in practice. This time, the focus is strictly on the ground-level details of the algorithms. You will be provided with all the skeleton code necessary to get started, so that you can focus solely on optimizing your algorithm.
With typical board games like chess, the two players in the game (i.e. the "Computer AI" and the "Player") take similar actions in their turn, and have similar objectives to achieve in the game. In the 2048-puzzle game, the setup is inherently asymmetric; that is, the computer and player take drastically different actions in their turns. Specifically, the computer is responsible for placing random tiles of 2 or 4 on the board, while the player is responsible for moving the pieces. However, adversarial search can be applied to this game just the same.
II. Algorithm Review
Before you begin, review the lecture slides on adversarial search. Is this a zero-sum game? What is the minimax principle? In the 2048-puzzle game, the computer AI is technically not "adversarial". In particular, all it does is spawn random tiles of 2 and 4 each turn, with a designated probability of either a 2 or a 4; it certainly does not specifically spawn tiles at the most inopportune locations to foil the player's progress. However, we will create a "Player AI" to play as if the computer is completely adversarial. In particular, we will employ the minimax algorithm in this assignment.
Remember, in game-playing we generally pick a strategy to employ. With the minimax algorithm, the strategy assumes that the computer opponent is perfect in minimizing the player's outcome. Whether or not the opponent is actually perfect in doing so is another question. As a general principle, how far the actual opponent's actual behavior deviates from the assumption certainly affects how well the AI performs [1]. However, you will see that this strategy works well in this game. In this assignment, we will implement and optimize the minimax algorithm.
[1] As we saw in the case of a simple game of tic-tac-toe, it is useful to employ the minimax algorithm, which assumes that the opponent is a perfect "minimizing" agent. In practice, however, we may encounter a sub-par opponent that makes silly moves. When this happens, the algorithm's assumption deviates from the actual opponent's behavior. In this case, it still leads to the desired outcome of never losing. However, if the deviation goes the other way (e.g. suppose we employ a "maximax" algorithm that assumes that the opponent wants us to win), then the outcome would certainly be different.
III. Using The Skeleton Code
To let you focus on the details of the algorithm, a skeleton code is provided to help you get started, and to allow you to test your algorithm on your own. The skeleton code includes the following files. Note that you will only be working in one of them, and the rest of them are read-only:
Read-only: GameManager.py. This is the driver program that loads your Computer AI and Player AI, and begins a game where they compete with each other. See below on how to execute this program.
Read-only: Grid.py. This module defines the Grid object, along with some useful operations: move(), getAvailableCells(), insertTile(), and clone(), which you may use in your code. These are available to get you started, but they are by no means the most efficient methods available. If you wish to strive for better performance, feel free to ignore these and write your own helper methods in a separate file.
Read-only: BaseAI.py. This is the base class for any AI component. All AIs inherit from this module, and implement the getMove() function, which takes a Grid object as parameter and returns a move (there are different "moves" for different AIs).
Read-only: ComputerAI.py. This inherits from BaseAI. The getMove() function returns a computer action that is a tuple (x, y) indicating the place you want to place a tile.
Writable: PlayerAI.py. You will create this file, and this is where you will be doing your work. This should inherit from BaseAI. The getMove() function, which you will need to implement, returns a number that indicates the player’s action. In particular, 0 stands for "Up", 1 stands for "Down", 2 stands for "Left", and 3 stands for "Right". You need to create this file and make it as intelligent as possible. You may include other files in your submission, but they will have to be included through this file.
Read-only: BaseDisplayer.py and Displayer.py. These print the grid.
To test your code, execute the game manager like so:
$ python3 GameManager.pyThe progress of the game will be displayed on your terminal screen, with one snapshot printed after each move that the Computer AI or Player AI makes. The Player AI is allowed 0.2 seconds to come up with each move. The process continues until the game is over; that is, until no further legal moves can be made. At the end of the game, the maximum tile value on the board is printed.
IMPORTANT: Do not modify the files that are specified as read-only. When your submission is graded, the grader will first automatically over-write all read-only files in the directory before executing your code. This is to ensure that all students are using the same game-play mechanism and computer opponent, and that you cannot "work around" the skeleton program and manually output a high score.
IV. What You Need To Submit
Your job in this assignment is to write PlayerAI.py, which intelligently plays the 2048-puzzle game. Here is a snippet of starter code to allow you to observe how the game looks when it is played out. In the following "naive" Player AI. The getMove() function simply selects a next move in random out of the available moves:
from random import randint
from BaseAI import BaseAI
class PlayerAI(BaseAI):
def getMove(self, grid):
moves = grid.getAvailableMoves()
return moves[randint(0, len(moves) - 1)] if moves else NoneOf course, that is indeed a very naive way to play the 2048-puzzle game. If you submit this as your finished product, you will likely receive a grade of zero. You should implement your Player AI with the following points in mind:
Employ the minimax algorithm. This is a requirement. There are many viable strategies to beat the 2048-puzzle game, but in this assignment we will be practicing with the minimax algorithm.
Implement alpha-beta pruning. This is a requirement. This should speed up the search process by eliminating irrelevant branches. In this case, is there anything we can do about move ordering?
Use heuristic functions. What is the maximum height of the game tree? Unlike elementary games like tic-tac-toe, in this game it is highly impracticable to search the entire depth of the theoretical game tree. To be able to cut off your search at any point, you must employ heuristic functions to allow you to assign approximate values to nodes in the tree. Remember, the time limit allowed for each move is 0.2 seconds, so you must implement a systematic way to cut off your search before time runs out.
Assign heuristic weights. You will likely want to include more than one heuristic function. In that case, you will need to assign weights associated with each individual heuristic. Deciding on an appropriate set of weights will take careful reasoning, along with careful experimentation. If you feel adventurous, you can also simply write an optimization meta-algorithm to iterate over the space of weight vectors, until you arrive at results that you are happy enough with.
V. Important Information
Please read the following information carefully. Before you post a clarifying question on the discussion board, make sure that your question is not already answered in the following sections.
1. Note on Python 3
.The current version of Python is 3.6.4.
2. Basic Requirements
Your submission must fulfill the following requirements:
You must use adversarial search in your PlayerAI (minimax with alpha-beta pruning).
You must provide your move within the time limit of 0.2 seconds.
You must name your file PlayerAI.py.
Your grade will depend on the maximum tile values your program usually gets to.
Before Submission
Make sure your code executes without fail on Vocareum. In particular, make sure you name your file correctly according to the instructions specified above.
Make sure your player achieves a satisfactory score on Vocareum. Your submission will be graded on the platform, where you may be allocated more or less processing power than your personal computer.
Make sure your PlayerAI.py does not print anything to the screen on Vocareum. Printing gameplay progress is handled by Grid.py, and there should ideally be nothing else printed. Due to the resource-intensive nature of this assignment, there is a limit on the throughput for each user's terminal on the platform. If you exceed the limit by printing more characters than it can handle, your process may get killed while the game is in progress, which would cause problems while grading.
You get unlimited submissions. There is a 30-minute time restriction in between submissions. By hitting the "SUBMIT" button on Vocareum, you are committing your work as the final product, and no further changes in your code will be considered. Depending on the grading load of the platform and the effectiveness of your code, it may take up to a day for your work to be graded completely. We are allowing several attempts at submitting your final work product. This is intended to accommodate for silly mistakes or accidents, or if you suddenly discover a novel way to improve your original algorithm and averaged scores after your initial submission. However, please test your code extensively before you submit
Skelton Code Files
class BaseAI:
def getMove(self, grid):
passclass BaseAI:
def getMove(self, grid):
passclass BaseDisplayer:
def __init__(self):
pass
def display(self, grid):
passclass BaseDisplayer:
def __init__(self):
pass
def display(self, grid):
passfrom random import randint
from BaseAI import BaseAI
class ComputerAI(BaseAI):
def getMove(self, grid):
cells = grid.getAvailableCells()
return cells[randint(0, len(cells) - 1)] if cells else Nonefrom random import randint
from BaseAI_3 import BaseAI
class ComputerAI(BaseAI):
def getMove(self, grid):
cells = grid.getAvailableCells()
return cells[randint(0, len(cells) - 1)] if cells else Nonefrom BaseDisplayer import BaseDisplayer
import platform
import os
colorMap = {
0 : 97 ,
2 : 40 ,
4 : 100,
8 : 47 ,
16 : 107,
32 : 46 ,
64 : 106,
128 : 44 ,
256 : 104,
512 : 42 ,
1024 : 102,
2048 : 43 ,
4096 : 103,
8192 : 45 ,
16384 : 105,
32768 : 41 ,
65536 : 101,
}
cTemp = "\x1b[%dm%7s\x1b[0m "
class Displayer(BaseDisplayer):
def __init__(self):
if "Windows" == platform.system():
self.display = self.winDisplay
else:
self.display = self.unixDisplay
def display(self, grid):
pass
def winDisplay(self, grid):
for i in xrange(grid.size):
for j in xrange(grid.size):
print "%6d " % grid.map[i][j],
print ""
print ""
def unixDisplay(self, grid):
for i in xrange(3 * grid.size):
for j in xrange(grid.size):
v = grid.map[i / 3][j]
if i % 3 == 1:
string = str(v).center(7, " ")
else:
string = " "
print cTemp % (colorMap[v], string),
print ""
if i % 3 == 2:
print ""from BaseDisplayer_3 import BaseDisplayer
import platform
import os
colorMap = {
0 : 97 ,
2 : 40 ,
4 : 100,
8 : 47 ,
16 : 107,
32 : 46 ,
64 : 106,
128 : 44 ,
256 : 104,
512 : 42 ,
1024 : 102,
2048 : 43 ,
4096 : 103,
8192 : 45 ,
16384 : 105,
32768 : 41 ,
65536 : 101,
}
cTemp = "\x1b[%dm%7s\x1b[0m "
class Displayer(BaseDisplayer):
def __init__(self):
if "Windows" == platform.system():
self.display = self.winDisplay
else:
self.display = self.unixDisplay
def display(self, grid):
pass
def winDisplay(self, grid):
for i in range(grid.size):
for j in range(grid.size):
print("%6d " % grid.map[i][j], end="")
print("")
print("")
def unixDisplay(self, grid):
for i in range(3 * grid.size):
for j in range(grid.size):
v = grid.map[int(i / 3)][j]
if i % 3 == 1:
string = str(v).center(7, " ")
else:
string = " "
print(cTemp % (colorMap[v], string), end="")
print("")
if i % 3 == 2:
print("")
from Grid import Grid
from ComputerAI import ComputerAI
from PlayerAI import PlayerAI
from Displayer import Displayer
from random import randint
import time
defaultInitialTiles = 2
defaultProbability = 0.9
actionDic = {
0: "UP",
1: "DOWN",
2: "LEFT",
3: "RIGHT"
}
(PLAYER_TURN, COMPUTER_TURN) = (0, 1)
# Time Limit Before Losing
timeLimit = 0.2
allowance = 0.05
class GameManager:
def __init__(self, size = 4):
self.grid = Grid(size)
self.possibleNewTiles = [2, 4]
self.probability = defaultProbability
self.initTiles = defaultInitialTiles
self.computerAI = None
self.playerAI = None
self.displayer = None
self.over = False
def setComputerAI(self, computerAI):
self.computerAI = computerAI
def setPlayerAI(self, playerAI):
self.playerAI = playerAI
def setDisplayer(self, displayer):
self.displayer = displayer
def updateAlarm(self, currTime):
if currTime - self.prevTime > timeLimit + allowance:
self.over = True
else:
while time.clock() - self.prevTime < timeLimit + allowance:
pass
self.prevTime = time.clock()
def start(self):
for i in xrange(self.initTiles):
self.insertRandonTile()
self.displayer.display(self.grid)
# Player AI Goes First
turn = PLAYER_TURN
maxTile = 0
self.prevTime = time.clock()
while not self.isGameOver() and not self.over:
# Copy to Ensure AI Cannot Change the Real Grid to Cheat
gridCopy = self.grid.clone()
move = None
if turn == PLAYER_TURN:
print "Player's Turn:",
move = self.playerAI.getMove(gridCopy)
print actionDic[move]
# Validate Move
if move != None and move >= 0 and move < 4:
if self.grid.canMove([move]):
self.grid.move(move)
# Update maxTile
maxTile = self.grid.getMaxTile()
else:
print "Invalid PlayerAI Move"
self.over = True
else:
print "Invalid PlayerAI Move - 1"
self.over = True
else:
print "Computer's turn:"
move = self.computerAI.getMove(gridCopy)
# Validate Move
if move and self.grid.canInsert(move):
self.grid.setCellValue(move, self.getNewTileValue())
else:
print "Invalid Computer AI Move"
self.over = True
if not self.over:
self.displayer.display(self.grid)
# Exceeding the Time Allotted for Any Turn Terminates the Game
self.updateAlarm(time.clock())
turn = 1 - turn
print maxTile
def isGameOver(self):
return not self.grid.canMove()
def getNewTileValue(self):
if randint(0,99) < 100 * self.probability:
return self.possibleNewTiles[0]
else:
return self.possibleNewTiles[1];
def insertRandonTile(self):
tileValue = self.getNewTileValue()
cells = self.grid.getAvailableCells()
cell = cells[randint(0, len(cells) - 1)]
self.grid.setCellValue(cell, tileValue)
def main():
gameManager = GameManager()
playerAI = PlayerAI()
computerAI = ComputerAI()
displayer = Displayer()
gameManager.setDisplayer(displayer)
gameManager.setPlayerAI(playerAI)
gameManager.setComputerAI(computerAI)
gameManager.start()
if __name__ == '__main__':
main()
from Grid_3 import Grid
from ComputerAI_3 import ComputerAI
from PlayerAI_3 import PlayerAI
from Displayer_3 import Displayer
from random import randint
import time
defaultInitialTiles = 2
defaultProbability = 0.9
actionDic = {
0: "UP",
1: "DOWN",
2: "LEFT",
3: "RIGHT"
}
(PLAYER_TURN, COMPUTER_TURN) = (0, 1)
# Time Limit Before Losing
timeLimit = 0.2
allowance = 0.05
class GameManager:
def __init__(self, size = 4):
self.grid = Grid(size)
self.possibleNewTiles = [2, 4]
self.probability = defaultProbability
self.initTiles = defaultInitialTiles
self.computerAI = None
self.playerAI = None
self.displayer = None
self.over = False
def setComputerAI(self, computerAI):
self.computerAI = computerAI
def setPlayerAI(self, playerAI):
self.playerAI = playerAI
def setDisplayer(self, displayer):
self.displayer = displayer
def updateAlarm(self, currTime):
if currTime - self.prevTime > timeLimit + allowance:
self.over = True
else:
while time.clock() - self.prevTime < timeLimit + allowance:
pass
self.prevTime = time.clock()
def start(self):
for i in range(self.initTiles):
self.insertRandonTile()
self.displayer.display(self.grid)
# Player AI Goes First
turn = PLAYER_TURN
maxTile = 0
self.prevTime = time.clock()
while not self.isGameOver() and not self.over:
# Copy to Ensure AI Cannot Change the Real Grid to Cheat
gridCopy = self.grid.clone()
move = None
if turn == PLAYER_TURN:
print("Player's Turn:", end="")
move = self.playerAI.getMove(gridCopy)
print(actionDic[move])
# Validate Move
if move != None and move >= 0 and move < 4:
if self.grid.canMove([move]):
self.grid.move(move)
# Update maxTile
maxTile = self.grid.getMaxTile()
else:
print("Invalid PlayerAI Move")
self.over = True
else:
print("Invalid PlayerAI Move - 1")
self.over = True
else:
print("Computer's turn:")
move = self.computerAI.getMove(gridCopy)
# Validate Move
if move and self.grid.canInsert(move):
self.grid.setCellValue(move, self.getNewTileValue())
else:
print("Invalid Computer AI Move")
self.over = True
if not self.over:
self.displayer.display(self.grid)
# Exceeding the Time Allotted for Any Turn Terminates the Game
self.updateAlarm(time.clock())
turn = 1 - turn
print(maxTile)
def isGameOver(self):
return not self.grid.canMove()
def getNewTileValue(self):
if randint(0,99) < 100 * self.probability:
return self.possibleNewTiles[0]
else:
return self.possibleNewTiles[1];
def insertRandonTile(self):
tileValue = self.getNewTileValue()
cells = self.grid.getAvailableCells()
cell = cells[randint(0, len(cells) - 1)]
self.grid.setCellValue(cell, tileValue)
def main():
gameManager = GameManager()
playerAI = PlayerAI()
computerAI = ComputerAI()
displayer = Displayer()
gameManager.setDisplayer(displayer)
gameManager.setPlayerAI(playerAI)
gameManager.setComputerAI(computerAI)
gameManager.start()
if __name__ == '__main__':
main()from sets import Set
from copy import deepcopy
directionVectors = (UP_VEC, DOWN_VEC, LEFT_VEC, RIGHT_VEC) = ((-1, 0), (1, 0), (0, -1), (0, 1))
vecIndex = [UP, DOWN, LEFT, RIGHT] = range(4)
class Grid:
def __init__(self, size = 4):
self.size = size
self.map = [[0] * self.size for i in xrange(self.size)]
# Make a Deep Copy of This Object
def clone(self):
gridCopy = Grid()
gridCopy.map = deepcopy(self.map)
gridCopy.size = self.size
return gridCopy
# Insert a Tile in an Empty Cell
def insertTile(self, pos, value):
self.setCellValue(pos, value)
def setCellValue(self, (x, y), value):
self.map[x][y] = value
# Return All the Empty c\Cells
def getAvailableCells(self):
cells = []
for x in xrange(self.size):
for y in xrange(self.size):
if self.map[x][y] == 0:
cells.append((x,y))
return cells
# Return the Tile with Maximum Value
def getMaxTile(self):
maxTile = 0
for x in xrange(self.size):
for y in xrange(self.size):
maxTile = max(maxTile, self.map[x][y])
return maxTile
# Check If Able to Insert a Tile in Position
def canInsert(self, pos):
return self.getCellValue(pos) == 0
# Move the Grid
def move(self, dir):
dir = int(dir)
if dir == UP:
return self.moveUD(False)
if dir == DOWN:
return self.moveUD(True)
if dir == LEFT:
return self.moveLR(False)
if dir == RIGHT:
return self.moveLR(True)
# Move Up or Down
def moveUD(self, down):
r = range(self.size -1, -1, -1) if down else range(self.size)
moved = False
for j in range(self.size):
cells = []
for i in r:
cell = self.map[i][j]
if cell != 0:
cells.append(cell)
self.merge(cells)
for i in r:
value = cells.pop(0) if cells else 0
if self.map[i][j] != value:
moved = True
self.map[i][j] = value
return moved
# move left or right
def moveLR(self, right):
r = range(self.size - 1, -1, -1) if right else range(self.size)
moved = False
for i in range(self.size):
cells = []
for j in r:
cell = self.map[i][j]
if cell != 0:
cells.append(cell)
self.merge(cells)
for j in r:
value = cells.pop(0) if cells else 0
if self.map[i][j] != value:
moved = True
self.map[i][j] = value
return moved
# Merge Tiles
def merge(self, cells):
if len(cells) <= 1:
return cells
i = 0
while i < len(cells) - 1:
if cells[i] == cells[i+1]:
cells[i] *= 2
del cells[i+1]
i += 1
def canMove(self, dirs = vecIndex):
# Init Moves to be Checked
checkingMoves = Set(dirs)
for x in xrange(self.size):
for y in xrange(self.size):
# If Current Cell is Filled
if self.map[x][y]:
# Look Ajacent Cell Value
for i in checkingMoves:
move = directionVectors[i]
adjCellValue = self.getCellValue((x + move[0], y + move[1]))
# If Value is the Same or Adjacent Cell is Empty
if adjCellValue == self.map[x][y] or adjCellValue == 0:
return True
# Else if Current Cell is Empty
elif self.map[x][y] == 0:
return True
return False
# Return All Available Moves
def getAvailableMoves(self, dirs = vecIndex):
availableMoves = []
for x in dirs:
gridCopy = self.clone()
if gridCopy.move(x):
availableMoves.append(x)
return availableMoves
def crossBound(self, (x, y)):
return x < 0 or x >= self.size or y < 0 or y >= self.size
def getCellValue(self, pos):
if not self.crossBound(pos):
return self.map[pos[0]][pos[1]]
else:
return None
if __name__ == '__main__':
g = Grid()
g.map[0][0] = 2
g.map[1][0] = 2
g.map[3][0] = 4
while True:
for i in g.map:
print i
print g.getAvailableMoves()
v = raw_input()
g.move(v)from copy import deepcopy
directionVectors = (UP_VEC, DOWN_VEC, LEFT_VEC, RIGHT_VEC) = ((-1, 0), (1, 0), (0, -1), (0, 1))
vecIndex = [UP, DOWN, LEFT, RIGHT] = range(4)
class Grid:
def __init__(self, size = 4):
self.size = size
self.map = [[0] * self.size for i in range(self.size)]
# Make a Deep Copy of This Object
def clone(self):
gridCopy = Grid()
gridCopy.map = deepcopy(self.map)
gridCopy.size = self.size
return gridCopy
# Insert a Tile in an Empty Cell
def insertTile(self, pos, value):
self.setCellValue(pos, value)
def setCellValue(self, pos, value):
self.map[pos[0]][pos[1]] = value
# Return All the Empty c\Cells
def getAvailableCells(self):
cells = []
for x in range(self.size):
for y in range(self.size):
if self.map[x][y] == 0:
cells.append((x,y))
return cells
# Return the Tile with Maximum Value
def getMaxTile(self):
maxTile = 0
for x in range(self.size):
for y in range(self.size):
maxTile = max(maxTile, self.map[x][y])
return maxTile
# Check If Able to Insert a Tile in Position
def canInsert(self, pos):
return self.getCellValue(pos) == 0
# Move the Grid
def move(self, dir):
dir = int(dir)
if dir == UP:
return self.moveUD(False)
if dir == DOWN:
return self.moveUD(True)
if dir == LEFT:
return self.moveLR(False)
if dir == RIGHT:
return self.moveLR(True)
# Move Up or Down
def moveUD(self, down):
r = range(self.size -1, -1, -1) if down else range(self.size)
moved = False
for j in range(self.size):
cells = []
for i in r:
cell = self.map[i][j]
if cell != 0:
cells.append(cell)
self.merge(cells)
for i in r:
value = cells.pop(0) if cells else 0
if self.map[i][j] != value:
moved = True
self.map[i][j] = value
return moved
# move left or right
def moveLR(self, right):
r = range(self.size - 1, -1, -1) if right else range(self.size)
moved = False
for i in range(self.size):
cells = []
for j in r:
cell = self.map[i][j]
if cell != 0:
cells.append(cell)
self.merge(cells)
for j in r:
value = cells.pop(0) if cells else 0
if self.map[i][j] != value:
moved = True
self.map[i][j] = value
return moved
# Merge Tiles
def merge(self, cells):
if len(cells) <= 1:
return cells
i = 0
while i < len(cells) - 1:
if cells[i] == cells[i+1]:
cells[i] *= 2
del cells[i+1]
i += 1
def canMove(self, dirs = vecIndex):
# Init Moves to be Checked
checkingMoves = set(dirs)
for x in range(self.size):
for y in range(self.size):
# If Current Cell is Filled
if self.map[x][y]:
# Look Ajacent Cell Value
for i in checkingMoves:
move = directionVectors[i]
adjCellValue = self.getCellValue((x + move[0], y + move[1]))
# If Value is the Same or Adjacent Cell is Empty
if adjCellValue == self.map[x][y] or adjCellValue == 0:
return True
# Else if Current Cell is Empty
elif self.map[x][y] == 0:
return True
return False
# Return All Available Moves
def getAvailableMoves(self, dirs = vecIndex):
availableMoves = []
for x in dirs:
gridCopy = self.clone()
if gridCopy.move(x):
availableMoves.append(x)
return availableMoves
def crossBound(self, pos):
return pos[0] < 0 or pos[0] >= self.size or pos[1] < 0 or pos[1] >= self.size
def getCellValue(self, pos):
if not self.crossBound(pos):
return self.map[pos[0]][pos[1]]
else:
return None
if __name__ == '__main__':
g = Grid()
g.map[0][0] = 2
g.map[1][0] = 2
g.map[3][0] = 4
while True:
for i in g.map:
print(i)
print(g.getAvailableMoves())
v = input()
g.move(v)
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