def _nextmove(self, State):
AIClient.ttentry = lambda self: State # Send State to the Transposition tables
AI = Negamax(6, tt=TT(), win_score=90
) # Algorithm(depth, scoring=None, win_score=inf,tt=None)
Quarto = AIClient([AI_Player(AI), AI_Player(AI)], State)
Move = Quarto.get_move() # find the best move possible
return json.dumps(Move) # send the Move
def make_move(self, move):
self.num_coins -= int(move)
def win_game(self):
return self.num_coins <= 0
def is_over(self):
return self.win()
def score(self):
return 100 if self.win_game() else 0
def show(self):
print(self.num_coins, 'coins left in the pile')
if __name__ == "__main__":
tt = TT()
LastCoin_game.ttentry = lambda self: self.num_coins
r, d, m = id_solve(LastCoin_game,
range(2, 20),
win_score=100,
tt=tt)
print(r, d, m)
game = LastCoin_game([AI_Player(tt), Human_Player()])
game.play()
class LastCoin_game(TwoPlayersGame): def __init__(self, players): self.players = players self.nplayer = 1 self.num_coins = 15 self.max_coins = 4 def possible_moves(self): return [str(a) for a in range(1, self.max_coins + 1)] #Define the removal of the coins . def make_move(self, move): self.num_coins -= int(move) #Define who took the last coin. def win_game(self): return self.num_coins <= 0 #Define when to stop the game, that is when somebody wins. def is_over(self): return self.win() #Define how to compute the score. def score(self): return 100 if self.win_game() else 0 #Define number of coins remaining in the pile. def show(self): print(self.num_coins, 'coins left in the pile') if __name__ == "__main__": tt = TT() #LastCoin_game.ttentry = lambda self: self.num_coins #Solving the game with the following code block: r, d, m = id_solve(LastCoin_game, range(2, 20), win_score=100, tt=tt) print(r, d, m) #Deciding who will start the game game = LastCoin_game([AI_Player(tt), Human_Player()]) game.play()
def _nextmove(self, state):
visible = state._state['visible']
move = {}
#List of the number of free places on the board
verif = []
for i in range(4):
verif.append([visible['board'][4 * i + e]
for e in range(4)].count(None))
verif.append([visible['board'][4 * e + i]
for e in range(4)].count(None))
verif.append([visible['board'][5 * e] for e in range(4)].count(None))
verif.append([visible['board'][3 + 3 * e]
for e in range(4)].count(None))
#First is a random AI
if verif.count(1) == 0:
ls_test = [i for i, x in enumerate(visible['board']) if x == None]
# select a random position
if visible['pieceToPlay'] is not None:
move['pos'] = random.choice(ls_test)
# select a random piece
test1 = visible['remainingPieces']
a = random.randint(0, len(test1) - 2)
move['nextPiece'] = a
# applymove will raise if we announce a quarto while there is not
move['quarto'] = True
try:
state.applymove(move)
except:
del (move['quarto'])
return json.dumps(move) # send the move
#When the number of free place per line/column/diag is brought down to 1, begin easyAI
else:
easyAI.ttentry = lambda self: state
ai_algo_neg = Negamax(6, tt=TT(), win_score=90) # Algorithm
Quarto = easyAI([AI_Player(ai_algo_neg),
AI_Player(ai_algo_neg)], state)
best_move = Quarto.get_move() # find the best move possible
print("BEST MOVE", best_move)
return json.dumps(best_move) # send the Move
def scoring(self):
return 100 if self.win() else 0
def ttentry(self):
return tuple(self.piles) # optional, speeds up AI
if __name__ == "__main__":
# IN WHAT FOLLOWS WE SOLVE THE GAME AND START A MATCH AGAINST THE AI
from easyAI import AI_Player, Human_Player, Negamax, id_solve
from easyAI.AI import TT
# we first solve the game
w, d, m, tt = id_solve(Nim, range(5, 20), win_score=80)
print
w, d, len(tt.d)
# the previous line prints -1, 16 which shows that if the
# computer plays second with an AI depth of 16 (or 15) it will
# always win in 16 (total) moves or less.
# Now let's play (and lose !) against the AI
ai = Negamax(16, tt=TT())
game = Nim([Human_Player(), AI_Player(tt)])
game.play() # You will always lose this game !
print("player %d wins" % game.nplayer)
# Note that with the transposition table tt generated by id_solve
# we can setup a perfect AI which doesn't have to think:
# >>> game = Nim( [ Human_Player(), AI_Player( tt )])
# >>> game.play() # You will always lose this game too!
def scoring(self):
return 100 if self.win() else 0
def show(self):
print("%d bones left in the pile" % (self.pile))
if __name__ == "__main__":
"""
Start a match (and store the history of moves when it ends)
ai = Negamax(10) # The AI will think 10 moves in advance
game = GameOfBones( [ AI_Player(ai), Human_Player() ] )
history = game.play()
"""
# Let's solve the game
from easyAI import id_solve, Human_Player, AI_Player
from easyAI.AI import TT
tt = TT()
GameOfBones.ttentry = lambda self: self.pile
r, d, m = id_solve(GameOfBones, range(2, 20), win_score=100, tt=tt)
print(r, d, m) # see the docs.
# Unbeatable AI !
game = GameOfBones([AI_Player(tt), Human_Player()])
game.play() # you will always lose this game :)
QuartoRandom(args.name, (args.host, args.port), verbose=args.verbose)
if args.component == 'prof':
ProfAI(args.name, (args.host, args.port), verbose=args.verbose)
if args.component == 'ai':
state = QuartoState()
if args.algo == 'solve':
if args.tt:
AIClient.ttentry = lambda self: state # Send State to the Transposition tables
result, depth, move = id_solve(AIClient([], state),
ai_depths=range(
2, int(args.depth)),
win_score=90,
tt=TT())
else:
result, depth, move = id_solve(AIClient([], state),
ai_depths=range(
2, int(args.depth)),
win_score=90)
print('result =', result)
print('depth =', depth)
print('move =', move)
if args.algo == 'Negamax':
if args.tt:
AI = Negamax(int(args.depth), tt=TT())
AIClient.ttentry = lambda self: state # Send State to the Transposition tables
else:
AI = Negamax(int(args.depth))
self.num_coins = 15
self.max_coins = 4
def possible_moves(self):
return [str(a) for a in range(1, self.max_coins + 1)]
def make_move(self, move):
self.num_coins -= int(move)
def win_game(self):
return self.num_coins <= 0
def is_over(self):
return self.win()
def score(self):
return 100 if self.win_game() else 0
def show(self):
print(self.num_coins, 'coins left in the pile')
if __name__ == "__main__":
tt: object = TT()
LastCoin_game.ttentry = lambda self: self.num_coins
r, d, m = id_solve(LastCoin_game,
range(2, 20), win_score=100, tt=tt)
print(r, d, m)
game = LastCoin_game([AI_Player(tt), Human_Player()])
game.play()