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 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()
def test_json_save_and_restore(self):
# 1. solve game/save TT
tt = easyAI.TT()
winner, depth, best_player_move = easyAI.id_solve(
examples.Nim,
range(13, 16),
tt=tt,
win_score=80,
verbose=False
)
tt.to_json_file("tt-data.json.temp", use_tuples=True)
# 2. restore TT from file
restored_tt = easyAI.TT()
restored_tt.from_json_file("tt-data.json.temp", use_tuples=True)
# 3. get first AI move using the TT
game = examples.Nim([easyAI.Human_Player(), easyAI.AI_Player(restored_tt)])
game.play_move(best_player_move) # let the human play
ai_move = game.get_move() # get the AI's move based on tt
self.assertEqual(ai_move, "2,5")
self.assertEqual(best_player_move, "1,5")
def test_json_save_and_restore(self):
# 1. solve game/save TT
tt = easyAI.TT()
winner, depth, best_player_move = easyAI.id_solve(examples.Nim,
range(13, 16),
tt=tt,
win_score=80,
verbose=False)
tt.to_json_file("tt-data.json.temp", use_tuples=True)
# 2. restore TT from file
restored_tt = easyAI.TT()
restored_tt.from_json_file("tt-data.json.temp", use_tuples=True)
# 3. get first AI move using the TT
game = examples.Nim(
[easyAI.Human_Player(),
easyAI.AI_Player(restored_tt)])
game.play_move(best_player_move) # let the human play
ai_move = game.get_move() # get the AI's move based on tt
self.assertEqual(ai_move, "2,5")
self.assertEqual(best_player_move, "1,5")
return self.win()
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 )])
def win(self): return max(self.piles) == 0
def is_over(self): return self.win()
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 )])
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 :)
return self.num_coins <= 0
# Stop the game when somebody wins
def is_over(self):
return self.win()
# Compute score
def scoring(self):
return 100 if self.win() else 0
# Show number of coins remaining in the pile
def show(self):
print(self.num_coins, 'coins left in the pile')
if __name__ == "__main__":
# Define the transposition table
tt = TT()
# Define the method
LastCoinStanding.ttentry = lambda self: self.num_coins
# Solve the game
result, depth, move = id_solve(LastCoinStanding,
range(2, 20), win_score=100, tt=tt)
print(result, depth, move)
# Start the game
game = LastCoinStanding([AI_Player(tt), Human_Player()])
game.play()
def solve_game():
tt = TT()
r, d, m = id_solve(Gomoku_optimized, range(2, 20), win_score=100, tt=tt)
print r, d, m
def is_over(self): return self.win() # game stops when someone wins.
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 :)
def solve_game():
tt = TT()
r, d, m = id_solve(Gomoku_Strategic, range(2, 7), win_score=100, tt=tt)
print r, d, m
def possible_moves(self):
return ['1', '2', '3']
def make_move(self, move):
self.pile -= int(move) # remove bones.
def win(self):
return self.pile <= 0 # opponent took the last bone ?
def is_over(self):
return self.win() # Game stops when someone wins.
def show(self):
print("%d bones left in the pile" % self.pile)
def scoring(self):
return 100 if self.win() else 0 # For the AI
r, d, m = id_solve(GameOfBones, ai_depths=range(2, 20), win_score=100)
tt = TT()
GameOfBones.ttentry = lambda game: game.pile # key for the table
r, d, m = id_solve(GameOfBones, range(2, 20), win_score=100, tt=tt)
game = GameOfBones([AI_Player(tt), Human_Player()])
history = game.play() # you will always lose this game :)
1
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)]
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(self) 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()
def solve_game():
#to run this method we need to modify the max score to 100(check with self.win())
tt = TT()
r, d, m = id_solve(Gomoku_Strategic, range(2, 20), win_score=100, tt=tt)
print r, d, m