def solve():
sets=[]
with open("ProblemList.txt") as r:
for line in r:
line = line.rstrip()
problem = Problem(line, '2x2')
sets.append(problem)
agent = Agent()
for problem in sets:
agent.Solve(problem)
def add_agents(self, nb):
old_length = len(self.agents)
for index in range(old_length, old_length + nb):
self.agents.append(
Agent(id_=index,
prediction_queue=self.prediction_queue,
training_queue=self.training_queue,
states=self.train_set,
exit_flag=Value(c_bool, False),
statistics_queue=self.statistics_queue,
episode_counter=self.nb_episodes,
observation_shape=(self.channels, self.height,
self.width),
action_space=self.n_outputs,
device=self.agent_device,
step_max=self.sequence_length))
def OnNewGame(self, event):
"""Creates a new game."""
playerTokenAnswer = self.PromptForToken(
) # Prompts user for his choice of token X or O.
if playerTokenAnswer == wx.ID_CANCEL: # Do nothing if user cancelled the NewGame..
return
if not hasattr(self, 'board'):
self.board = TicTacToe()
self.agent = Agent()
self.mainGrid = TTTPanel(self)
self.Layout()
else:
self.ClearMainGrid()
self.board.clear()
self.over = False
if playerTokenAnswer == wx.ID_YES: # Player chose 'X'.
self.agent.token = 'O'
self.SetStatusText("X to start!")
else: # Player chose 'O'.
self.agent.token = 'X'
self.SetStatusText("Computer is thinking...")
self.AgentMove()
self.SetStatusText("O to move!")
parser = argparse.ArgumentParser()
parser.add_argument('-s', dest='size', type=int)
parser.add_argument('-b', dest='blanks', action='store_true')
parser.add_argument('-e', dest='expectimax', action='store_true')
parser.add_argument('-p', dest='prune', action='store_true')
results = parser.parse_args()
if not results.size:
results.size = 15
rules = ScrabbleRules(blanks=results.blanks, size=results.size)
state = GameState(blanks=results.blanks, size=results.size)
view = View()
agent_0 = Agent()
agent_1 = Agent()
state.add_agent(0, agent_0)
state.add_agent(1, agent_1)
state.place('A', [(results.size // 2, results.size // 2)], 0, rules)
state.draw(0)
state.draw(1)
# Play
agents = [0, 1]
try:
while True:
for agent in agents:
state.draw(agent)
best_move = None
if results.expectimax:
from src.Agent import Agent
from src.TicTacToe import TicTacToe
board1 = ['X', 'X', ' ', 'O', 'O', ' ', ' ', ' ', ' ']
drawn_board = ['X', 'X', 'O', 'O', 'O', 'X', 'X', 'X', 'O']
board_O = ['X', 'X', 'O', 'X', 'O', ' ', 'O', ' ', ' ']
O_to_win = ['X', 'X', 'O', 'X', 'O', ' ', ' ', ' ', ' ']
newgame = TicTacToe()
game1 = TicTacToe(board1)
drawn_game = TicTacToe(drawn_board)
game_o = TicTacToe(board_O)
game_o_to_win = TicTacToe(O_to_win)
agentx = Agent()
agento = Agent(token='O')
def test_eval_game_over():
assert agentx.eval_game_over(drawn_game) == 0
assert agentx.eval_game_over(game_o) == -10
assert agento.eval_game_over(game_o) == 10
def test_eval():
assert agentx.eval(game1) == 9
assert agento.eval(game1) == -9
def test_choose_move():
assert agentx.choose_move(game1) == 2
import argparse
import time
import sys
sys.path.insert(0, 'src')
from src.Agent import Agent
if __name__ == '__main__':
ap = argparse.ArgumentParser()
ap.add_argument('-b',
'--behaviour',
type=str,
required=True,
help='behaviour name')
ap.add_argument('-m',
'--minutes',
type=int,
required=True,
help='activate behaviour on X minutes')
ap.add_argument('-d',
'--delay',
type=int,
default=5,
help='delay in seconds')
args = vars(ap.parse_args())
time.sleep(args['delay'])
agent = Agent()
agent.AddBehaviour(args['behaviour'], args['minutes'])
agent.Activate()
parser = argparse.ArgumentParser()
parser.add_argument(
"-m",
"--mode",
default=available_modes[0],
choices=available_modes,
dest="mode",
help=
"""Running mode of AI ["TACTICS", "MATCHES", "TEST_TACTICS", "TEST_MATCHES"]"""
)
parser.add_argument("-a",
"-ai",
default="TestModel",
choices=list(available_networks.keys()),
dest="ai_model",
help="""Select AI Models""")
parser.add_argument("-e",
"--epochs",
default=1,
type=int,
dest="epochs",
help="""Count of Epochs""")
args = parser.parse_args()
agent = Agent(ai_name=args.ai_model,
mode=args.mode,
epochs=args.epochs,
players=ConstantGame.count_teams())
agent.run()