def undo_available_placement(self):
# we just need to pop each piece from the undo_moves effected pieces
while len(self.undo_effected) > 0:
action = self.undo_effected.pop()
#print("POP")
#print(action)
loc = action[0]
#print(loc)
colour = action[1]
undo_type = action[2]
opponent = Board.get_opp_piece_type(colour)
if undo_type == constant.ELIMINATED_PIECE:
# this piece was eliminated before the undo move, now we have placed it back on the board with undo
if loc in self.available_actions[colour]:
# remove the action from the dictionary of the corresponding colour
self.available_actions[colour].pop(loc)
if loc in self.available_actions[opponent]:
self.available_actions[opponent].pop(loc)
elif undo_type == constant.PLACE_LOC:
# a piece was was placed at this location at prior to calling undo move
# therefore to reestablish the original available moves list, then we need to add
# this piece to the corresponding dict
if loc not in self.available_actions[colour] and loc not in\
self.available_actions[opponent]:
# if we can place a piece at this location again -- then this piece corresponds to a free space
if self.board.within_starting_area(loc, colour):
temp = {loc: constant.PLACEMENT_PHASE}
self.available_actions[colour].update(temp)
if self.board.within_starting_area(loc, opponent):
temp = {loc: constant.PLACEMENT_PHASE}
self.available_actions[opponent].update(temp)
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 __init__(self, board, colour):
# we want to create a node
self.tt = TranspositionTable()
# only use this board to complete the search
# save memory
self.board = deepcopy(board)
# for alpha beta search -- instead of passing it into the function calls we can use this
self.alpha = -inf
self.beta = inf
# defines the colours of min and max
self.player = colour
self.opponent = Board.get_opp_piece_type(self.player)
# default depth
self.depth = inf
# default move ordering with iterative deepening
self.actions_evaluated = []
self.actions_leftover = []
# data structures for machine learning
self.eval_depth = 0
self.minimax_val = 0
self.policy_vector = []
# dictionary storing the available moves of the board
self.available_actions = {
constant.WHITE_PIECE: {},
constant.BLACK_PIECE: {}
}
# generate the actions for the start of the game
# self.generate_actions()
self.undo_effected = []
self.time_alloc = 0
self.time_rem = 0
self.time_start = 0
self.time_end = 0
self.evaluation = Evaluation("./XML", "/eval_weights")
from Agents.Minimax import Minimax
# create a new board game
board_game = Board()
board_game.print_board()
# create the starting node -- this node is black
node = Minimax.create_node(board_game, constant.BLACK_PIECE, None)
node.board.print_board()
print(node.available_moves)
print(node.colour)
print()
# to create a child node we apply one of the move from black to the next node
child = Minimax.create_node(node.board,Board.get_opp_piece_type(node.colour),node.available_moves[2])
child.board.print_board()
print(len(child.available_moves))
print(child.colour)
print()
print("UNDO MOVE")
child.board.undo_move()
child.board.print_board()
print(child.board.eliminated_pieces)
print(child.board.piece_pos)
child = Minimax.create_node(child.board,Board.get_opp_piece_type(child.colour),child.available_moves[3])
child.board.print_board()
print(len(child.available_moves))
print(child.colour)
def negamax(self, depth, alpha, beta, colour):
# Timeout handling
self.time_end = self.curr_millisecond_time()
if self.time_end - self.time_start > self.time_rem:
raise TimeOut
opponent = Board.get_opp_piece_type(colour)
original_alpha = alpha
dic = {self.player: 1, self.opponent: -1}
'''
move_to_try = None
# check if the current board state is in the transposition table
board_str = self.board.board_state.decode("utf-8")
key = self.tt.contains(board_str,colour,phase=self.board.phase)
if key is not None:
board_str = key[0]
entry = self.tt.get_entry(board_str,colour)
tt_value = entry[0]
tt_type = entry[1]
tt_best_move = entry[2]
tt_depth = entry[3]
# if we have found an entry in the transposition table, then the move
# we should try first is this best move
move_to_try = tt_best_move
#print("FOUND ENTRY IN TT")
if tt_depth >= depth:
if tt_type == constant.TT_EXACT:
#print("FOUND PV")
return tt_value, tt_best_move
elif tt_type == constant.TT_LOWER:
if tt_value > alpha:
#print("FOUND FAIL SOFT")
alpha = tt_value
elif tt_type == constant.TT_UPPER:
if tt_value < beta:
#print("FOUND FAIL HARD")
beta = tt_value
if alpha >= beta:
return tt_value, None
'''
# terminal test -- default case
if self.cutoff_test(depth):
val = self.evaluate_state(self.board, self.player) #*dic[colour]
return val, None
# do the minimax search
best_val = -inf
best_action = None
actions = self.board.update_actions(self.board, colour)
'''
if move_to_try is not None and move_to_try in actions:
#print("MOVE ORDERING")
# put the move to try at the first position -- therefore it will be searched first
actions = [move_to_try] + actions
i = 0
'''
# get the favourable moves of the board
actions = self.get_favourable_actions(self.available_actions)
# if there are no favourable actions to iterate on - raise
if len(actions) < 0:
raise ReturnUnfavourableMove
for action in actions:
# skip over the best action in the tt table
'''
if action == move_to_try and i!= 0:
continue
i+=1
'''
self.board.update_board(action, colour)
score, temp = self.negamax(depth - 1, -beta, -alpha, opponent)
score = -score
if score > best_val:
best_val = score
best_action = action
if score > alpha:
alpha = score
self.undo_move()
if alpha >= beta:
break
'''
# store the values in the transposition table
if best_val <= original_alpha:
# then this is an upperbound -FAILHARD
tt_type = constant.TT_UPPER
elif best_val >= beta:
tt_type = constant.TT_LOWER
# print("LOWER")
else:
tt_type = constant.TT_EXACT
# print("EXACT")
'''
# add the entry to the transposition table
# self.tt.add_entry(self.board.board_state,colour,best_val,tt_type,best_action, depth)
return best_val, best_action
def set_player_colour(self, colour):
self.player = colour
self.opponent = Board.get_opp_piece_type(colour)
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
def negascout(self, depth, colour):
# Timeout handling
self.time_end = self.curr_millisecond_time()
if self.time_end - self.time_start > self.time_rem:
raise TimeOut
opponent = Board.get_opp_piece_type(colour)
alpha = -inf
original_alpha = alpha
beta = inf
dic = {self.player: 1, self.opponent: -1}
move_to_try = None
# check if the current board state is in the transposition table
board_str = self.board.board_state.decode("utf-8")
key = self.tt.contains(board_str, colour, phase=self.board.phase)
if key is not None:
board_str = key[0]
entry = self.tt.get_entry(board_str, colour)
tt_value = entry[0]
tt_type = entry[1]
tt_best_move = entry[2]
tt_depth = entry[3]
# if we have found an entry in the transposition table, then the move
# we should try first is this best move
move_to_try = tt_best_move
# print(move_to_try)
# print("FOUND ENTRY IN TT")
if tt_depth >= depth:
if tt_type == constant.TT_EXACT:
# print("FOUND PV")
return tt_best_move
elif tt_type == constant.TT_LOWER:
if tt_value > alpha:
# print("FOUND FAIL SOFT")
alpha = tt_value
elif tt_type == constant.TT_UPPER:
if tt_value < beta:
# print("FOUND FAIL HARD")
beta = tt_value
if alpha >= beta:
return tt_best_move
actions_1 = self.board.update_actions(self.board, colour)
# actions = actions_1
actions = self.board.sort_actions(actions_1, colour)
# actions = actions_1
# terminal test -- default case
if self.cutoff_test(depth):
val = self.evaluate_state(self.board, self.player,
actions_1) * dic[colour]
return val, None
# do the minimax search
best_val = -inf
best_action = None
if move_to_try is not None and move_to_try in actions:
# print("MOVE ORDERING")
# put the move to try at the first position -- therefore it will be searched first
actions = [move_to_try] + actions
i = 0
if len(actions) <= 20:
favourable = actions
else:
favourable = actions[:20]
# print(len(actions))
# start negascout here
for i, action in enumerate(favourable):
# skip over the best action in the tt table
if action == move_to_try and i > 0:
continue
self.board.update_board(action, colour)
'''
if i == 0:
# do a full search on the best move found so far
score, _ = self.negascout(depth-1,-beta,-alpha, opponent)
score = -score
else:
# assume that
score, _ = self.negascout(depth-1,-alpha-1,-alpha,opponent)
score = -score
if alpha < score < beta:
score, _ = self.negascout(depth-1,-beta,-score,opponent)
score = -score
'''
score = -self.negascout_value(depth - 1, -beta, -alpha, opponent)
if alpha < score < beta and i > 0:
score = -self.negascout_value(depth - 1, -beta, -alpha,
opponent)
if best_val < score:
best_val = score
best_action = action
if alpha < score:
alpha = score
self.undo_move()
if alpha >= beta:
break
beta = alpha + 1
# store the values in the transposition table
if best_val <= original_alpha:
# then this is an upperbound -FAILHARD
tt_type = constant.TT_UPPER
elif best_val >= beta:
tt_type = constant.TT_LOWER
# print("LOWER")
else:
tt_type = constant.TT_EXACT
# print("EXACT")
# add the entry to the transposition table
self.tt.add_entry(self.board.board_state, colour, best_val, tt_type,
best_action, depth)
return best_action
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
node.board.print_board()
print(node.available_moves)
print(node.colour)
print()
# to create a child node we apply one of the move from black to the next node
child = Minimax.create_node(node.board, node.colour, (3, 2))
child.board.print_board()
print(child.available_moves)
print(child.board.move_counter)
print(child.board.piece_pos)
print(child.board.eliminated_pieces)
child = Minimax.create_node(child.board,
Board.get_opp_piece_type(child.colour), (3, 3))
child.board.print_board()
print(child.available_moves)
print(child.board.move_counter)
print(child.board.piece_pos)
print(child.board.eliminated_pieces)
child = Minimax.create_node(child.board,
Board.get_opp_piece_type(child.colour), (6, 2))
child.board.print_board()
print(child.available_moves)
print(child.board.move_counter)
print(child.board.piece_pos)
print(child.board.eliminated_pieces)
child = Minimax.create_node(child.board,
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
def negamax(self, depth, alpha, beta, colour):
# Timeout handling
self.time_end = self.curr_millisecond_time()
if self.time_end - self.time_start > self.time_rem:
raise TimeOut
opponent = Board.get_opp_piece_type(colour)
dic = {self.player: 1, self.opponent: -1}
# generate legal actions
actions_1 = self.board.update_actions(self.board, colour)
# print(len(actions))
actions = self.board.sort_actions(actions_1, colour)
# terminal test -- default case
if self.cutoff_test(depth):
val = self.evaluate_state(self.board, self.player,
actions) * dic[colour]
return val, None
# do the minimax search
best_val = -inf
best_action = None
# generate legal actions
#actions = self.board.update_actions(self.board, colour)
# split the actions into favourable an unfavourable
# if the length of actions is greater than X, then we can just choose to look through the first
# 5 'favourable' actions that we see right now
# if the length of actions is less than X, then we can just evaluate all possible actions we have
# THIS IS A GREEDY APPROACH TO MINIMAX THAT LIMITS OUR BRANCHING FACTOR OF THE GAME
if len(actions) > 8:
favourable = actions[:8]
else:
favourable = actions
# got here
#print("got here")
# depth reduction
R = 2
#print(favourable)
#self.board.print_board()
for action in favourable:
self.board.update_board(action, colour)
if action in favourable:
score, temp = self.negamax(depth - 1, -beta, -alpha, opponent)
else:
score, temp = self.negamax(depth - 1 - R, -beta, -alpha,
opponent)
score = -score
if score > best_val:
best_val = score
best_action = action
if score > alpha:
alpha = score
self.undo_move()
if alpha >= beta:
break
return best_val, best_action
