class Player:
def __init__(self, colour):
if colour == 'white':
self.colour = constant.WHITE_PIECE
elif colour == 'black':
self.colour = constant.BLACK_PIECE
self.available_moves = []
# each players internal board representation
self.board = Board()
# TODO -- need to see if this works correctly
self.strategy = MonteCarloTreeSearch(self.board, self.colour)
self.opponent = self.board.get_opp_piece_type(self.colour)
def update(self, action):
# update the board based on the action of the opponent
if self.board.phase == constant.PLACEMENT_PHASE:
# update board also returns the pieces of the board that will be eliminated
self.board.update_board(action, self.opponent)
# self.board.eliminated_pieces[self.opponent]
elif self.board.phase == constant.MOVING_PHASE:
if isinstance(action[0], tuple) is False:
print("ERROR: action is not a tuple")
return
move_type = self.board.convert_coord_to_move_type(
action[0], action[1])
# update the player board representation with the action
self.board.update_board((action[0], move_type), self.opponent)
def action(self, turns):
self.strategy.num_nodes = 0
self.strategy.update_board(self.board)
if turns == 0 and self.board.phase == constant.MOVING_PHASE:
self.board.move_counter = 0
self.board.phase = constant.MOVING_PHASE
best_move = self.strategy.MCTS()
# print("NUM NODE IN THIS TREE: " + str(self.strategy.num_nodes))
# once we have found the best move we must apply it to the board representation
if self.board.phase == constant.PLACEMENT_PHASE:
self.board.update_board(best_move, self.colour)
return best_move
else:
new_pos = Board.convert_move_type_to_coord(best_move[0],
best_move[1])
self.board.update_board(best_move, self.colour)
return best_move[0], new_pos
def playgame(self, first_player=True):
game = Board()
state_index = 0
if first_player:
# this player makes the move first
self.game_state.append(deepcopy(game))
while game.is_terminal() is False:
turns = game.move_counter
action = self.player.action(turns)
# append the depth at which the action was evaluated at -- this is so we can find the best child node later
# we require r(s_i^l,w) -- now is s_i^l the minimax value that evaluated?
self.eval_depths.append(self.player.depth_eval)
self.minimax_eval.append(self.player.minimax_val)
self.policy_vectors.append(self.player.policy_vector)
# update the board
game.update_board(action, self.player.colour)
self.opponent.update(action)
turns = game.move_counter
# opponent makes a move
action = self.opponent.action(turns)
game.update_board(action, self.opponent.colour)
self.player.update(action)
# add the game state to the game state array
self.game_state.append(deepcopy(game))
# return the outcome of the game
if game.winner == constant.WHITE_PIECE:
return 1
elif game.winner == constant.BLACK_PIECE:
return -1
elif game.winner is None:
return 0
class Player:
def __init__(self, colour):
if colour == 'white':
self.colour = constant.WHITE_PIECE
elif colour == 'black':
self.colour = constant.BLACK_PIECE
self.available_moves = []
# each players internal board representation
self.board = Board()
# initialise the available moves
self.init_start_moves()
# TODO -- need to see if this works correctly
self.minimax = MinimaxABUndo(self.board)
self.opponent = self.board.get_opp_piece_type(self.colour)
# self.search_algorithm = Minimax(self.board,self.available_moves,self.colour)
# print(self.opponent)
# set up the board for the first time
def init_start_moves(self):
# set the initial board parameters
# no pieces on the board
# available moves is the entire starting zone for each player
if self.colour == constant.WHITE_PIECE:
# set the white pieces available moves
for row in range(0, constant.BOARD_SIZE - 2):
for col in range(constant.BOARD_SIZE):
if (row, col) not in self.board.corner_pos:
self.available_moves.append((col, row))
else:
# set the black piece available moves
for row in range(2, constant.BOARD_SIZE):
for col in range(constant.BOARD_SIZE):
if (row, col) not in self.board.corner_pos:
# append the available move in the list in the form col, row
self.available_moves.append((col, row))
def update(self, action):
# print("UPDATING THIS ACTION : " + str(action))
if self.board.move_counter == 0:
# then the opponent is the first person to move
self.board.set_player_to_move(self.opponent)
# update the board based on the action of the opponent
# get move type
if self.board.phase == constant.PLACEMENT_PHASE:
# update board also returns the pieces of the board that will be eliminated
self.board.update_board(action, self.opponent)
# self.board.eliminated_pieces[self.opponent]
self.minimax.update_board(self.board)
# remove the opponent piece from the available moves list
elif self.board.phase == constant.MOVING_PHASE:
if isinstance(action[0], tuple) is False:
print("asdfasf")
return
move_type = self.board.convert_coord_to_move_type(
action[0], action[1])
# print("MOVETYPE: " + str(move_type))
# print(action[0])
self.board.update_board((action[0], move_type), self.opponent)
#self.minimax.update_available_actions(action,self.opponent)
def action(self, turns):
if turns == 0 and self.board.phase == constant.PLACEMENT_PHASE:
self.board.set_player_to_move(self.colour)
if turns == 24 and self.board.phase == constant.PLACEMENT_PHASE:
self.board.move_counter = 0
self.board.phase = constant.MOVING_PHASE
root = self.minimax.create_node(self.colour, None)
self.minimax.update_minimax_board(None, root)
# self.minimax.update_available_actions(None)
# best_move = self.minimax.alpha_beta_minimax(2,root)
# best_move = self.minimax.iterative_deepening_alpha_beta(root)
best_move = self.minimax.alpha_beta_minimax(3, root)
# do an alpha beta search on this node
if self.board.phase == constant.PLACEMENT_PHASE:
# print(best_move)
self.board.update_board(best_move, self.colour)
self.minimax.update_board(self.board)
return best_move
else:
# (best_move is None)
# print(best_move[0],best_move[1])
new_pos = Board.convert_move_type_to_coord(best_move[0],
best_move[1])
self.board.update_board(best_move, self.colour)
self.minimax.update_board(self.board)
return best_move[0], new_pos
class Player:
def __init__(self, colour):
if colour == 'white':
self.colour = constant.WHITE_PIECE
elif colour == 'black':
self.colour = constant.BLACK_PIECE
self.available_moves = []
# each players internal board representation
self.board = Board()
# TODO -- need to see if this works correctly
self.minimax = Negamax(self.board, self.colour)
self.opponent = self.board.get_opp_piece_type(self.colour)
# self.search_algorithm = Minimax(self.board,self.available_moves,self.colour)
# print(self.opponent)
self.depth_eval = 0
self.minimax_val = 0
self.policy_vector = 0
def update(self, action):
# update the board based on the action of the opponent
if self.board.phase == constant.PLACEMENT_PHASE:
# update board also returns the pieces of the board that will be eliminated
self.board.update_board(action, self.opponent)
# self.board.eliminated_pieces[self.opponent]
self.minimax.update_board(self.board)
elif self.board.phase == constant.MOVING_PHASE:
if isinstance(action[0], tuple) is False:
print("ERROR: action is not a tuple")
return
move_type = self.board.convert_coord_to_move_type(action[0], action[1])
# update the player board representation with the action
self.board.update_board((action[0], move_type), self.opponent)
self.minimax.update_board(self.board)
def action(self, turns):
self.minimax.update_board(self.board)
# print(self.board.piece_pos)
# if action is called first the board representation move counter will be zero
# this indicates that this player is the first one to move
# if update is called before action the board representation counter will be 1,
# this indicates that the player is the second to move
if turns == 0 and self.board.phase == constant.MOVING_PHASE:
self.board.move_counter = 0
self.board.phase = constant.MOVING_PHASE
# create the node to search on
# update the board representation and the available moves
# print(self.minimax.available_actions)
# best_move = self.minimax.alpha_beta_minimax(3)
best_move = self.minimax.itr_negamax()
# best_move = self.minimax.alpha_beta(3)
self.depth_eval = self.minimax.eval_depth
self.minimax_val = self.minimax.minimax_val
# do an alpha beta search on this node
# once we have found the best move we must apply it to the board representation
if self.board.phase == constant.PLACEMENT_PHASE:
# print(best_move)
self.board.update_board(best_move, self.colour)
self.minimax.update_board(self.board)
return best_move
else:
if best_move is None:
return None
# (best_move is None)
# print(best_move[0],best_move[1])
new_pos = Board.convert_move_type_to_coord(best_move[0], best_move[1])
self.board.update_board(best_move, self.colour)
self.minimax.update_board(self.board)
return best_move[0], new_pos
class Player:
def __init__(self, colour):
if colour == 'white':
self.colour = constant.WHITE_PIECE
elif colour == 'black':
self.colour = constant.BLACK_PIECE
self.available_moves = []
# each players internal board representation
self.board = Board()
# initialise the available moves
self.init_start_moves()
self.opponent = self.board.get_opp_piece_type(self.colour)
# print(self.opponent)
# set up the board for the first time
def init_start_moves(self):
# set the initial board parameters
# no pieces on the board
# available moves is the entire starting zone for each player
if self.colour == constant.WHITE_PIECE:
# set the white pieces available moves
for row in range(0, constant.BOARD_SIZE - 2):
for col in range(constant.BOARD_SIZE):
if (row, col) not in self.board.corner_pos:
self.available_moves.append((col, row))
else:
# set the black piece available moves
for row in range(2, constant.BOARD_SIZE):
for col in range(constant.BOARD_SIZE):
if (row, col) not in self.board.corner_pos:
# append the available move in the list in the form col, row
self.available_moves.append((col, row))
def update(self, action):
# print("UPDATING THIS ACTION : " + str(action))
if self.board.move_counter == 0:
# then the opponent is the first person to move
self.board.set_player_to_move(self.opponent)
# update the board based on the action of the opponent
# get move type
if self.board.phase == constant.PLACEMENT_PHASE:
# update board also returns the pieces of the board that will be eliminated
self.board.update_board(action, self.opponent)
eliminated_pieces = self.board.eliminated_pieces[self.opponent]
# remove the eliminated pieces from the available moves of this player
for piece in eliminated_pieces:
if piece in self.available_moves and Player.within_starting_area(
piece, self.colour):
# self.available_moves.remove(piece)
self.available_moves.append(piece)
# remove the opponent piece from the available moves list
if action in self.available_moves:
self.available_moves.remove(action)
# print(self.available_moves)
elif self.board.phase == constant.MOVING_PHASE:
if isinstance(action[0], tuple) is False:
# print("WHYYYYYYYY")
return
move_type = self.board.convert_coord_to_move_type(
action[0], action[1])
# print("MOVETYPE: " + str(move_type))
# print(action[0])
self.board.update_board((action[0], move_type), self.opponent)
def action(self, turns):
# print("TURNS SO FAR ---------- " + str(turns))
# print("ACTION CALLED: BOARD REPRESENTATION COUNTER: " + str(self.board.move_counter))
if turns == 0 and self.board.phase == constant.PLACEMENT_PHASE:
# then we are first person to move
self.board.set_player_to_move(self.colour)
if turns < 24 and self.board.phase == constant.PLACEMENT_PHASE:
# then we pick the best move to make based on a search algorithm
search_algorithm = Random(len(self.available_moves))
next_move = self.available_moves[search_algorithm.choose_move()]
# making moves during the placement phase
self.board.update_board(next_move, self.colour)
eliminated_pieces = self.board.eliminated_pieces[self.colour]
# remove the move made from the available moves
self.available_moves.remove(next_move)
if len(eliminated_pieces) != 0:
for piece in eliminated_pieces:
if piece in self.available_moves:
self.available_moves.remove(piece)
return next_move
elif self.board.phase == constant.MOVING_PHASE:
if turns == 0 or turns == 1:
# if the turns is 0 or 1 and the board is in moving phase then the
# all players have placed their pieces on the board, we can call update_available_moves to update the
# available moves available to this player
# clear the list
self.available_moves = []
# update the lists available moves -- now in the form ((col,row),move_type)
# self.update_available_moves()
# print(self.available_moves)
# we are making a move in the moving phase
#print(self.available_moves)
self.update_available_moves()
# if there are no available moves to be made we can return None:
if len(self.available_moves) == 0:
return None
# print("AVAILABLE MOVES: " + str(self.colour) + " " + str(self.available_moves))
# if there is a move to be made we can return the best move
# TODO : THIS IS WHERE WE CARRY OUT OUR SEARCH ALGORITHM
# then we pick the best move to make based on a search algorithm
search_algorithm = Random(len(self.available_moves))
next_move = self.available_moves[search_algorithm.choose_move()]
self.board.update_board(next_move, self.colour)
new_pos = self.board.convert_move_type_to_coord(
next_move[0], next_move[1])
# print(self.colour + " " + str(self.board.piece_pos))
# TODO - need to double check if this update_available_moves is necessary
self.update_available_moves()
#print(getsizeof(self.board.piece_pos))
#print(getsizeof(self.board.board_state))
#print(getsizeof(self.available_moves))
return next_move[0], new_pos
# updates the available moves a piece can make after it has been moved
# this way we don;t need to calculate all the available moves on the board
# as pieces that have been eliminated also get rid of those associated available moves
def update_available_moves(self):
# clear the available moves
available_moves = []
self.available_moves = []
# recalculate the moves a piece can make based on the available pieces on the board
# print(self.colour)
# print("-"*20)
# self.board.print_board()
# print("-"*20)
# print("THIS PLAYERS CURRENT PIECES: " + str(self.colour) + str(self.board.piece_pos[self.colour]))
for piece in self.board.piece_pos[self.colour]:
for move_type in range(constant.MAX_MOVETYPE):
if self.board.is_legal_move(piece, move_type):
available_moves.append((piece, move_type))
self.available_moves = available_moves
@staticmethod
def within_starting_area(move, colour):
if colour == constant.WHITE_PIECE:
# update the starting rows based off the player colour
if colour == constant.WHITE_PIECE:
min_row = 0
max_row = 6
elif colour == constant.BLACK_PIECE:
min_row = 2
max_row = 8
col, row = move
if min_row <= row <= max_row:
return True
else:
return False