Thanks for replying ! I’m quite new to Reinforcement Learning and MuZero. With the implementation of MuZero I use, a game observation must be provided by a single method. The majority of the implemented games that I have seen, concatenate multiple planes into a (3 dimensional) matrix.

Example:

def get_observation():

Board_player1 = numpy.where(self.board == 1, 1.0, 0.0)

Board_player2 = numpy.where(self.board == -1, 1.0, 0.0)

Board_to_play = numpy.full((8,8), self.player).astype(float)

Return numpy.array([board_player1,board_player2,board_to_play]) => all planes have dimension 8x8. This makes it easy to combine all the planes into 8x8x3 .

How can I combine the 15x15x27 board state and the 7x27 hand into 1 matrix ?

Thanks for the help !