def getAction(self, gameState, index, validActions):
if gameState.isLose():
return None
actions = gameState.getLegalActions(index, validActions)
if index == 0: # human player
unrolled = util.unroll_board(gameState.board)
x = numpy.asarray(unrolled)
x = x.reshape(1, x.shape[0])
if self.feature_transform:
x = numpy.log2(x)
predictions = [(self.predict[move](x), move) for move in range(4)]
predictions.sort(reverse=True)
if predictions[0][1] in validActions:
return predictions[0][1]
else:
print('Falling back to second choice since {} was last moved'.format(predictions[0][1]))
return predictions[1][1]
else:
return None if len(actions) == 0 else random.choice(actions)
def write_move(self, board, move):
self.inputs_writer.writerow(util.unroll_board(board))
self.labels_writer.writerow((move, ))
def eval_snake(state):
'''
Linear combination of all squares' values.
Inspired by Hadi Pouransari & Saman Ghili's "AI algorithms for the game 2048".
'''
return util.dot_product(SNAKE_WEIGHTS, util.unroll_board(state.board))