37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
from cup import Cup
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import numpy as np
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import tensorflow as tf
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from network import Network
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from board import Board
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import random
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class RestoreBot:
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def __init__(self, sym):
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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(graph = self.graph)
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self.network = Network(self.session)
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self.network.restore_model()
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def get_sym(self):
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return self.sym
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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 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 = moves_and_scores[np.array(scores).argmax()][0]
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#print("Found the best state, being:", np.array(move_scores).argmax())
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return best_move
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# return random.choice(list(legal_moves))
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