78 lines
2.4 KiB
Python
78 lines
2.4 KiB
Python
from cup import Cup
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import tensorflow as tf
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from network import Network
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import numpy as np
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from board import Board
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import subprocess
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import random
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import sys
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class Bot:
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def __init__(self, sym, config = None):
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self.config = config
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self.cup = Cup()
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self.sym = sym
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self.graph = tf.Graph()
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with self.graph.as_default():
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self.session = tf.Session()
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self.network = Network(self.session, config)
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self.network.restore_model()
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def roll(self):
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print("{} rolled: ".format(self.sym))
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roll = self.cup.roll()
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# print(roll)
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return roll
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def switch(self,cur):
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return -1 if cur == 1 else 1
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def restore_model(self):
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with self.graph.as_default():
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self.network.restore_model()
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def get_session(self):
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return self.session
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def get_sym(self):
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return self.sym
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def get_network(self):
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return self.network
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def make_random_move(self, board, sym, roll):
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legal_moves = Board.calculate_legal_states(board, sym, roll)
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return random.choice(list(legal_moves))
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# TODO: Test this, the score results should be deterministic
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def make_pubeval_move(self, board, sym, roll):
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legal_moves = Board.calculate_legal_states(tuple(board), sym, roll)
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moves_and_scores = []
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for board in legal_moves:
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call_argument = ["./pubeval/pubeval"]
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for x in Board.board_features_to_pubeval(board, sym):
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call_argument.append(str(x))
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data = subprocess.check_output(call_argument)
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moves_and_scores.append([board, float(bytes.decode(data))])
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scores = [ x[1] for x in moves_and_scores ]
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best_move_pair = moves_and_scores[np.array(scores).argmax()]
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return best_move_pair
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def make_move(self, board, sym, roll):
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# print(Board.pretty(board))
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legal_moves = Board.calculate_legal_states(board, sym, roll)
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moves_and_scores = [ (move, self.network.eval_state(np.array(move).reshape(1,26))) for move in legal_moves ]
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scores = [ x[1] for x in moves_and_scores ]
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best_move_pair = moves_and_scores[np.array(scores).argmax()]
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#print("Found the best state, being:", np.array(move_scores).argmax())
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return best_move_pair
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