class federatedplayer:
def __init__(self, p):
# get the model from the server
model = self._download_model()
# create an evaluator with these weights, and an ai agent using it
evaluator = TutorialEvaluator(p, model)
# TTT's constructor takes in the piece
# that STARTS first (not the player's piece)
self.board = TTT(3, START_PIECE, evaluator)
# self.agent = AlphaBeta(4)
self.agent = AlphaBeta()
def update(self, move):
self._apply(move)
def move(self):
move = self._move()
self._apply(move)
return move
def _apply(self, move):
self.board.update(move)
def _move(self):
return self.agent.next_move(self.board)
def _download_model(self):
server = flip.flip()
server.connect()
server.send_check()
model = server.recv_model()
server.disconnect()
return model
def move(self, state):
""" Takes the output of the Alpha-Beta Minimax algorithm and uses it
to tell the game where to place it's chip """
ab = AlphaBeta(state)
move = ab.next_move(self.difficulty, state, self.chip)
return move