def dijkstraEval(self, board_state, maximizer):
h = HexBoard(board_state.shape[0])
h.board = board_state
red_shortestPath = self.dijkstra(hexboard=h, color=h.RED)
blue_shortestPath = self.dijkstra(hexboard=h, color=h.BLUE)
freeReds = self.getFreeHexes(hexboard=h, path=red_shortestPath)
freeBlues = self.getFreeHexes(hexboard=h, path=blue_shortestPath)
heuristic_score = 0
if maximizer == h.RED:
heuristic_score = freeBlues - freeReds
else:
heuristic_score = freeReds - freeBlues
return heuristic_score
# def iterativeDeepening(self, node, maximizer):
# depth = 1
# end_time = datetime.now() + timedelta(seconds=3)
# For windows os
# while datetime.now() < end_time and not msvcrt.kbhit():
# bestScore = self.alphaBetaSearch(node, depth, -9999999, 9999999, isMaximizer=maximizer)
# depth = depth + 1
# return bestScore
def _update_board(board: HexBoard, l_move, is_max: bool) -> HexBoard:
board = copy.deepcopy(
board
) # I think this was the problem with the minimax core, it was using a reference instead of a deep copy
color = board.BLUE if is_max else board.RED
board.place(l_move, color)
return board
def alphabeta_Id(board: HexBoard, depth: int, alpha: float, beta: float,
is_max: bool) -> float:
# board.print()
print("in alphabeta_Id000000000")
try:
(hit, g, ttbm) = transposition_table.lookup(board,
board.get_board_size(),
depth)
print("in alphabeta_Id", hit, g, ttbm)
except Exception as e: #
print('Exception in running lookup function: ' + str(e))
if hit():
return g
if depth == 0 or board.is_game_over():
g = dijkstra_eval(board)
bm = ()
legals = board.get_move_list()
if legals:
if is_max:
g: float = -_INF
for move in ttbm + legals:
updated_board: HexBoard = _update_board(
board, move, is_max) # y do we make the move first?
gc = alphabeta_Id(updated_board, depth - 1, alpha, beta,
is_max)
if gc > g:
bm = updated_board
g = gc
alpha = max(alpha, g)
if beta <= alpha:
break
else: # if is_max False
g: float = _INF
for move in ttbm + legals:
updated_board: HexBoard = _update_board(board, move, is_max)
gc = alphabeta_Id(updated_board, depth - 1, alpha, beta,
is_max)
if gc < g:
bm = updated_board
g = gc
beta = min(beta, g)
if beta <= alpha:
break
transposition_table.store(updated_board,
updated_board.get_board_size(), g, depth, bm)
return g
else:
print("NO MORE LEGAL MOVES LEFT")
return dijkstra_eval(board)
def simple_dijkstra(board: HexBoard, source, is_max):
Q = set()
V_set = board.get_all_vertices()
dist = {}
dist_clone = {} # I made a clone of dist to retain the information in dist
prev = {}
for v in V_set:
dist[v] = np.inf
dist_clone[v] = np.inf # clone
prev[v] = None
Q.add(v)
dist[source] = 0
dist_clone[source] = dist[source] # clone
while len(Q) != 0:
u = min(dist, key=dist.get)
Q.remove(u)
color = board.BLUE if is_max else board.RED
## IGNORE THE CODE BELOW, IT DOESN'T WORK PROPERLY AT THE MOMENT
# begin reverse iteration step
# this happens when we get to a border of the corresponding color (i.e. we connect the sides)
# if board.border(color, u):
# S = []
# path_length = 0
# if prev[u] or u == source:
# while u:
# if not board.is_color(u, color):
# path_length += 1
# S.insert(0, u)
# u = prev[u]
# print(f"S for color {color} IS: {S}")
# print(f"Path length when accounting for already marked hexagons: {path_length}")
# return path_length, S # You can also optionally return "S" if you also want to store the path
# # end reverse iteration step
neighbors = board.get_neighbors(u)
for v in neighbors:
if v in Q: # Only check neighbours that are also in "Q"
len_u_v = 0 if board.is_color(
v,
color) else 1 # this isn't working as intended i think...
alt = dist[u] + len_u_v
if alt < dist[v]:
dist[v] = alt
dist_clone[v] = dist[v]
prev[v] = u
# We pop "u" from the distance dict to ensure that the keys match the ones in "Q"
dist.pop(
u
) # This is also why we need the clone, or else we'll return an empty dict
return dist_clone, prev
def _update_board(board: HexBoard, l_move, is_max: bool) -> HexBoard:
"""
Makes a deep copy of the board and updates the board state on that copy.
This makes it so that we don't need to use an undo move function.
The reason for using deepcopy is because python passes objects by reference
if you use the "=" operator
"""
board = copy.deepcopy(
board
) # I think this was the problem with the minimax core, it was using a reference instead of a deep copy
color = board.BLUE if is_max else board.RED
board.place(l_move, color)
return board
def alphabeta(board: HexBoard, depth: int, alpha: float, beta: float,
is_max: bool) -> float:
# board.print()
if depth == 0 or board.is_game_over():
return dijkstra_eval(board)
legals = board.get_move_list()
if legals:
if is_max:
g: float = -_INF
for move in legals:
updated_board: HexBoard = _update_board(board, move, is_max)
g = max(
g,
alphabeta(updated_board,
depth - 1,
alpha,
beta,
is_max=False))
alpha = max(alpha, g)
if beta <= alpha:
break
else:
g: float = _INF
for move in legals:
updated_board: HexBoard = _update_board(board, move, is_max)
g = min(
g,
alphabeta(updated_board,
depth - 1,
alpha,
beta,
is_max=True))
beta = min(beta, g)
if beta <= alpha:
break
return g
else:
print("NO MORE LEGAL MOVES LEFT")
return dijkstra_eval(board)
def alphabeta_move(board: HexBoard, depth: int, is_max: bool, show_AI=False):
"""
Set is_max to True for BLUE player, and False for RED player.
You can set the depth to whatever you want really, just don't go too deep it'll take forever.
Set show_AI to True if you want to see it's scoring process
"""
legal_moves = board.get_move_list()
best_score = -np.inf
best_move = None
for move in legal_moves:
sim_board = _update_board(board, move, is_max)
if sim_board.check_win(
sim_board.BLUE if is_max else sim_board.RED
): # KILLER MOVE: If we find a move in the simulation that wins, make that move no matter what
if show_AI: print(f"KILLER MOVE FOUND: {move}")
best_move = move
best_score = np.inf
break
score = alphabeta(
sim_board, depth=depth, alpha=-np.inf, beta=np.inf, is_max=is_max
) # For some reason performs better if you use is_max=False
if show_AI: print(f"CURRENT SCORE: {score} for MOVE: {move}")
if score > best_score:
best_score = score
best_move = move
if show_AI: print(f"BEST MOVE: {best_move} with BEST SCORE: {best_score}")
return best_move
def Expand(self, node, game_state):
"""
Function: Adds a random child to node,
Updates game_state and node's untriedMoves
Returns : The created child
"""
untriedMoves = node.untriedMoves
m = choice(untriedMoves)
# node's untriedMoves updated here
node.untriedMoves.remove(m)
# game_state updated here
game_state = HexBoard.makeMove(m, game_state)
# Check the child, if it is already appended return it otherwise create that child
child = node.getChild(game_state)
if child is not None:
return child
child_type = 'MIN'
if node.type == 'MIN':
child_type = 'MAX'
child = Node(node_type=child_type,
board_state=game_state.board,
parent=node)
# node's children updated here
node.children.append(child)
return child
def dijkstraEval(self, board_state, maximizer):
h = HexBoard(board_state.shape[0])
h.board = board_state
red_shortestPath = self.dijkstra(hexboard=h, color=h.RED)
blue_shortestPath = self.dijkstra(hexboard=h, color=h.BLUE)
freeReds = self.getFreeHexes(hexboard=h, path=red_shortestPath)
freeBlues = self.getFreeHexes(hexboard=h, path=blue_shortestPath)
if maximizer == h.RED:
heuristic_score = freeBlues - freeReds
else:
heuristic_score = freeReds - freeBlues
return heuristic_score
def simple_dijkstra(board: HexBoard, source, is_max):
Q = set()
V_set = board.get_all_vertices()
dist = {}
dist_clone = {} # I made a clone of dist to retain the information in dist
prev = {}
for v in V_set:
dist[v] = np.inf
dist_clone[v] = np.inf # clone
prev[v] = None
Q.add(v)
dist[source] = 0
dist_clone[source] = dist[source] # clone
while len(Q) != 0:
u = min(dist, key=dist.get)
Q.remove(u)
color = board.BLUE if is_max else board.RED
neighbors = board.get_neighbors(u)
for v in neighbors:
if v in Q: # Only check neighbours that are also in "Q"
len_u_v = -1 if board.is_color(
v,
color) else 1 # this isn't working as intended i think...
### BLOCK TO MAKE AI MORE AGGRESSIVE ###
if board.border(
color, v
): # If there is a move that reaches the border in the simulation
if board.check_win(color): # And it results in a win
len_u_v = -2 # Make that move more valuable
### END OF AGGRO BLOCK ###
alt = dist[u] + len_u_v
if alt < dist[v]:
dist[v] = alt
dist_clone[v] = dist[v]
prev[v] = u
# We pop "u" from the distance dict to ensure that the keys match the ones in "Q"
dist.pop(
u
) # This is also why we need the clone, or else we'll return an empty dict
return dist_clone, prev
def get_shortest_path(board: HexBoard, distances, color):
"""Gets the shortest path to a border and returns the length as score"""
borders = board.get_borders(color)
# print(f"ALL BORDERS: {borders}")
shortest = np.inf
for border in borders:
# print(f"Dist for current border {border} is: {distances[border]}")
if distances[border] < shortest:
shortest = distances[border]
# print(f"Current shortest: {shortest}")
return shortest
def MCTS_Player(hyperparameters, isMaximizer):
# First Computer's turn (MCTS)
mcts_agent = MCTS(
game=game, cp=hyperparameters[0],
n=hyperparameters[1]) # initialize mcts agent
move, params['node'] = mcts_agent.search(
params['node'], hyperparameters[2], hyperparameters[3],
isMaximizer)
params['game'] = HexBoard.makeMove(move, params['game'])
params['node'] = util.updateNode(params['node'],
params['game'])
def minimax(board: HexBoard, depth: int, is_max: bool) -> float:
if depth == 0 or board.is_game_over():
board.print()
return dijkstra_eval(board)
legals = board.get_move_list()
if legals:
if is_max:
g: float = -_INF
for move in legals:
updated_board: HexBoard = _update_board(board, move, is_max)
g = max(g, minimax(updated_board, depth - 1, not is_max))
else:
g: float = _INF
for move in legals:
updated_board: HexBoard = _update_board(board, move, is_max)
g = min(g, minimax(updated_board, depth - 1, not is_max))
return g
def PlayervsComputer(self):
game = self.game
self.clearOutput()
game.print()
node = Node(node_type='MIN', board_state=game.board,
parent=None) # 1 - initialize node
mcts_agent = MCTS(game=game, cp=0.8,
n=100) # 3 - initialize mcts agent
util = UTIL(infinity=np.inf,
maximizer=game.maximizer,
minimizer=game.minimizer) # 4 - initialize util class
while not game.isGameOver():
if (game.turn % 2) == 0:
move = self.getReady(game)
game = HexBoard.makeMove(move, game)
node = util.updateNode(node, game)
else:
print("Computer is thinking!!!")
itermax = 100 # maximum iteration for search
move, node = mcts_agent.search(node,
itermax,
delta=10,
isMaximizer=True)
print(f'best move: {move}')
game = HexBoard.makeMove(move, game)
node = util.updateNode(node, game)
self.clearOutput()
game.print()
if game.isGameOver():
if game.checkWin(game.BLUE):
print("!!! Blue Player Won !!!")
elif game.checkWin(game.RED):
print("!!! Red Player Won !!!")
def initGame(b_size, starter):
#clearOutput()
h = HexBoard(b_size)
if starter == h.RED:
h.maximizer = h.BLUE
h.minimizer = h.RED
else:
h.maximizer = h.RED
h.minimizer = h.BLUE
return h
def IDTT_Player(hyperparameters, isMaximizer):
# iterative deepening with 4 depth-Dijkstra
boardState = params['node'].board.copy()
try:
move = IterativeDeepeningTranspositionTable[
boardState.tobytes()]
except KeyError:
best_value = util.iterativeDeepening(
params['node'], isMaximizer, hyperparameters[0],
hyperparameters[1])
move = util.getBestMove(params['node'], best_value)
IterativeDeepeningTranspositionTable[
boardState.tobytes()] = move
params['game'] = HexBoard.makeMove(move, params['game'])
params['node'] = util.updateNode(params['node'],
params['game'])
def alphabeta_move_Id(board: HexBoard,
is_max: bool,
show_AI=False): #, depth: int
"""
Set is_max to True for BLUE player, and False for RED player.
You can set the depth to whatever you want really, just don't go too deep it'll take forever.
Set show_AI to True if you want to see it's scoring process
"""
legal_moves = board.get_move_list()
print("num of legal moves", len(legal_moves))
best_score = -np.inf
best_move = None
for move in legal_moves:
sim_board = _update_board(board, move, is_max)
if sim_board.check_win(
sim_board.BLUE if is_max else sim_board.RED
): # KILLER MOVE: If we find a move in the simulation that wins, make that move no matter what
if show_AI: print(f"KILLER MOVE FOUND: {move}")
best_move = move
best_score = np.inf
break
#thread = threading.Thread(target=iterative_deepening, kwargs=dict(board = sim_board,alpha=-np.inf,beta = np.inf, is_max = is_max))
#thread.start()
with concurrent.futures.ThreadPoolExecutor() as executor:
future = executor.submit(iterative_deepening,
sim_board,
alpha=-np.inf,
beta=np.inf,
is_max=is_max)
score = future.result()
print("score ", score)
# wait here for the result to be available before continuing
#thread.join()
#score = iterative_deepening(sim_board, alpha=-np.inf, beta=np.inf,
# is_max=is_max) # For some reason performs better if you use is_max=False
if show_AI: print(f"ID CURRENT SCORE: {score} for MOVE: {move}")
if score > best_score:
best_score = score
best_move = move
if show_AI:
print(f"BEST MOVE_ID: {best_move} with BEST SCORE: {best_score}")
return best_move
def getBestMove(node, bestVal):
bestChildren = []
for child in node.children:
if child.value == bestVal:
bestChildren.append(child)
if len(bestChildren) > 0:
index = np.random.randint(0, len(bestChildren))
bestNode = bestChildren[index]
else:
# print("Error, best Move couldn't find. Randomly selects the move!")
moves = HexBoard.getMoveList(node)
index = np.random.randint(0, len(moves))
return moves[index]
b_size = node.board.shape[0]
for i in range(b_size):
for j in range(b_size):
if node.board[i, j] != bestNode.board[i, j]:
return i, j
def __init__(self, node_type, board_state, parent):
self.id = str(uuid.uuid4()) # to make it unique
self.name = self.id[
-3:] # to make it simple during visualizations (for the test purposes)
self.type = node_type # for visualization
self.board = board_state # for MCTS
untriedMoves = HexBoard.getFreeMoves(board_state)
self.untriedMoves = untriedMoves # for MCTS
self.children = [] # for MCTS
if parent is not None:
self.parents = [parent]
self.parent_type = parent.type # for visualization
else:
self.parents = [] # for MCTS
self.parent_type = None # for visualization
self.searched = False
self.value = None
self.visit = 0 # for MCTS UCT-Selection and finding the best move
self.wins = np.inf # for MCTS UCT-Selection
self.loss = np.inf
def dijkstra_eval(board: HexBoard):
"""
Checks the best path from every possible source (e.g. L to R for blue) and
then returns the best evaluation score of the board based on the most
efficient source
"""
best_eval_score = -np.inf
for i in range(board.get_board_size()):
blue_source = (0, i)
red_source = (i, 0)
blue_dists, _ = simple_dijkstra(board, blue_source, True)
red_dists, _ = simple_dijkstra(board, red_source, False)
blue_score = get_shortest_path(board, blue_dists, board.BLUE)
red_score = get_shortest_path(board, red_dists, board.RED)
eval_score = red_score - blue_score
if eval_score > best_eval_score:
best_eval_score = eval_score
return best_eval_score
def initGame():
clearOutput()
b_size = int(input("Enter the board size you want to play: "))
h = HexBoard(b_size)
ans = str(
input("In order to start the game, please choose a side 'R' or 'B': ")
).lower()
if ans == 'b':
h.maximizer = h.RED
h.minimizer = h.BLUE
elif ans == 'r':
h.maximizer = h.BLUE
h.minimizer = h.RED
else:
print("You pressed a wrong key. Please type 'R' or 'B'.")
print("Game is restarting!!!")
time.sleep(2)
h = initGame()
return h
def main():
board = HexBoard(2)
num_of_cells = board.get_board_size() * board.get_board_size()
for nc in range(int(num_of_cells / 2)):
## Just a small heuristic for opening strategy, you can test this if you want. But then you have to comment the move_blue below out too
# if board.size % 2 != 0 and len(board.get_move_list()) == len(board.get_all_vertices()): # If it's the first move and the board is uneven
# move_blue = (board.size // 2, board.size // 2) # Always place the first move in the middle
# else:
# move_blue = ab.alphabeta_move(board, depth=2, is_max=True)
move_blue = ab.alphabeta_move(board, depth=4, is_max=True)
#move_blue = ab.alphabeta_move_Id(board, is_max=True, show_AI=True)
board = ab._update_board(board, move_blue, is_max=True)
board.print()
if board.is_game_over(
): # TODO: add condition for game over without no winning (board full)
print("==== BLUE WINS ====")
board.print()
# break
return "blue"
move_red = ab.alphabeta_move(board, depth=2, is_max=True)
#move_red = ab.alphabeta_move_Id(board, is_max=True, show_AI=True)
board = ab._update_board(
board, move_red, is_max=False
) # Using false here and true for the alphabeta is a bit confusing, but we need it to make moves for red here.
board.print()
if board.is_game_over(
): # TODO: add condition for game over without no winning (board full)
print("==== RED WINS ====")
board.print()
# break
return "red"
import numpy as np
from hex_skeleton import HexBoard
# sanity check that wins are detected
for i in range(0, 2):
winner = HexBoard.RED if i == 0 else HexBoard.BLUE
loser = HexBoard.BLUE if i == 0 else HexBoard.RED
board = HexBoard(3)
board.place((1, 1), loser)
board.place((2, 1), loser)
board.place((1, 2), loser)
board.place((2, 2), loser)
board.place((0, 0), winner)
board.place((1, 0), winner)
board.place((2, 0), winner)
board.place((0, 1), winner)
board.place((0, 2), winner)
assert (board.check_win(winner) == True)
assert (board.check_win(loser) == False)
board.print()
endable_board = HexBoard(4)
# sanity check that random play will at some point end the game
while not endable_board.game_over:
endable_board.place((np.random.randint(0, 4), np.random.randint(0, 4)),
HexBoard.RED)
assert (endable_board.game_over == True)
assert (endable_board.check_win(HexBoard.RED) == True)
assert (endable_board.check_win(HexBoard.BLUE) == False)
print("Randomly filled board")
endable_board.print()
f.write(f'd = {d} g = {g} a = {a} b = {b}\n')
f.close()
board.make_empty((i, j))
#virtual_board.print()
if a >= g:
break
if d == search_depth:
f = open('movelist.txt', 'a')
f.write(f'\n{search.d2l_conversion(best_move[0])},{best_move[1]}')
f.close()
return g
# Initialise the board.
board = HexBoard(BOARD_SIZE)
# Create a virtual board for the alphabeta algorithm.
virtual_board = board
# Make a text file for the moves.
f = open('movelist.txt', 'w')
f.write('Movelist')
f.close()
while not board.game_over:
search_depth = 3
eval_val = alphabeta(virtual_board,
d=search_depth,
a=-INF,
b=INF,
df_movelist = df_movelist.append({'x': x, 'y': y}, ignore_index=True)
board.place((x, y), agent2)
board.print()
# Play the game.
def play_game(board, method):
for i in range(SIZE * SIZE):
board_copy = copy.deepcopy(board)
eval_val = alphabeta(board_copy, DEPTH, -INF, INF, agent1, agent2,
method)
df_alphabeta.to_csv('alphabeta.txt', index=False, mode='a')
ai_make_move(board)
if board.check_win(agent1):
print('A1 WINS')
break
pl_make_move(board)
if board.check_win(agent2):
print('A2 WINS')
break
DEPTH = 3
board = HexBoard(SIZE)
play_game(board, 'Dijkstra')
# Agents Matchup 2: [depth = 3, policy = random] vs. [depth = 4, policy = dijkstra]
# Agents Matchup 3: [depth = 3, policy = dijkstra] vs. [depth = 4, policy = dijkstra]
print("[Initalized Player Matchup: Experiment 1]")
print("[---------------------------------------------------------]")
res_lst_1 = []
res_elo_1 = []
res_elo_2 = []
res_elo_1.append(player1_ratings.mu)
res_elo_2.append(player2_ratings.mu)
n_games = 25 # number of games
size = 5 # board size
board = HexBoard(size) # init board
player_1_lst = [
player(agent='AI', color=HexBoard.BLUE, policy="random"),
player(agent='AI', color=HexBoard.BLUE, policy="random"),
player(agent="AI",
color=HexBoard.BLUE,
policy="alphabeta",
eval="dijkstra",
depth=3)
]
player_2_lst = [
player(agent="AI",
color=HexBoard.BLUE,
policy="alphabeta",
def human_compu(algor, method='random', depth=3):
"""A function that human play with computer, user can choose their prefered
board size(3,4 recommanded), color, want to take first move"""
global flag, INF, n_nodes, cutoff, Game, size
print('Choose a board size:')
size = int(input())
Game = HexBoard(size)
print('Choose a color: 1(BLUE) or 2(RED)')
print('Blue: from left to right; Red: from top to bottom')
opp_color = int(input())
my_color = Game.get_opposite_color(opp_color)
print('Do you want to start first? Yes(y) or No(n)')
first = input()
print('Game start!')
# human first move do or not
if first == 'y' or first == 'Yes':
Game.print()
x, y = xy_read(Game)
Game.place(coordinates=(x, y), color=opp_color)
Game.print()
root = Tree(Game,
color=opp_color,
parent=None,
coordinate=(x, y),
my_color=my_color)
else:
first_color = my_color
last_color = opp_color
root = Tree(Game,
color=opp_color,
parent=None,
coordinate=None,
my_color=my_color)
INF = 99999 # sufficient large number
# human and computer play the game until one of them win
while not Game.is_game_over():
if algor == 'alphabeta':
# varibales intialization
n_nodes = 0
cutoff = 0
flag = 0
my_move = alphabeta(n=root,
a=-INF,
b=INF,
d=depth,
method=method,
depth=depth,
my_color=my_color,
opp_color=opp_color)
print('n_nodes=', n_nodes, '\n cutoff=', cutoff)
elif algor == 'idtt':
flag = 0
transpositiontable = []
n_nodes = 0
cutoff = 0
my_move = iterativedeepening(n=root,
a=-INF,
b=INF,
DEPTH_stop=5,
time_limit=5,
my_color=my_color,
opp_color=opp_color)
print('n_nodes=', n_nodes, '\n cutoff=', cutoff)
elif algor == "mcts":
my_move = monte_carlo_tree_search(root, cp=1, N=1000)
# retunred variable "my_move" is a node and contains info about computer's next step
Game.place(coordinates=my_move.location, color=my_move.color)
Game.print()
if Game.is_game_over():
break
else:
# read human's next move
x, y = xy_read(Game)
Game.place(coordinates=(x, y), color=opp_color)
Game.print()
root = Tree(Game,
color=opp_color,
parent=None,
coordinate=(x, y),
my_color=my_color)
if Game.check_win(opp_color):
print('Game over! You win :-)')
else:
print('Game over! You lose :-(')
def mcts_mcts(cp1, N1, cp2, N2, size, print_all=False, first=True):
#mcts plays with mcts
Game = HexBoard(size)
A1_color = 1
A2_color = 2
root = Tree(Game, color=A2_color, parent=None, coordinate=None)
if print_all:
if first == True:
print('First move (blue) is mcts')
print('Second move (red) is mcts')
else:
print('First move (blue) is mcts')
print('Second move (red) is mcts')
while not Game.is_game_over():
if first == True:
#mcts moves first
A1_move = monte_carlo_tree_search(root, cp=cp1, N=N1)
else:
A1_move = monte_carlo_tree_search(root, cp=cp2, N=N2)
Game.place(coordinates=A1_move.location, color=A1_color)
if Game.is_game_over():
if print_all:
Game.print()
if first == True:
#if method1 win, return True
return Game.check_win(A1_color)
else:
#if method1 win, return True
return Game.check_win(A2_color)
else:
root = Tree(Game,
color=A1_color,
parent=None,
coordinate=A1_move.location)
if first == True:
A2_move = monte_carlo_tree_search(root, cp=cp2, N=N2)
else:
A2_move = monte_carlo_tree_search(root, cp=cp1, N=N1)
Game.place(coordinates=A2_move.location, color=A2_move.color)
root = Tree(Game,
color=A2_color,
parent=None,
coordinate=A2_move.location)
if print_all:
Game.print()
if first == True:
#if method1 win or not
return Game.check_win(A1_color)
else:
return Game.check_win(A2_color)
def alphaBetaSearchDijkstra(self, node, depth, alpha, beta, isMaximizer):
node['searched'] = True
if depth == 0 or node['type'] == 'LEAF':
# if depth == 0, then it is already a leaf node
node['type'] = 'LEAF'
#node['value'] = self.dummyEval()
# calculate the value
if isMaximizer:
node['value'] = self.dijkstraEval(node['board'], 1)
else:
node['value'] = self.dijkstraEval(node['board'], 2)
return node['value']
# If we already get this nodes children then this section we be skipped
if len(node['children']) <= 0:
# get children of this node
children = []
moves = HexBoard.getMoveList(node)
# If there is no possible move then this is a leaf node
if len(moves) == 0:
# Node type is changing!!!!
node['type'] = 'LEAF'
if isMaximizer:
node['value'] = self.dijkstraEval(node['board'], 1)
else:
node['value'] = self.dijkstraEval(node['board'], 2)
return node['value']
else:
for move in moves:
b = node['board'].copy()
if isMaximizer:
b[move] = self.MAXIMIZER
else:
b[move] = self.MINIMIZER
child_id = str(uuid.uuid4())
node_type = 'MIN'
if node['type'] == 'MIN':
node_type = 'MAX'
child = {
'id': child_id,
'type': node_type,
'children': [],
'parent_type': node['type'],
'searched': False,
'board': b,
'value': None,
'name': child_id[-3:]
}
children.append(child)
node['children'] = children
if isMaximizer:
bestVal = -self.INF
for n in node['children']:
bestVal = max(
bestVal,
self.alphaBetaSearchDijkstra(n, depth - 1, alpha, beta,
False))
alpha = max(alpha, bestVal) # Updating alpha
if bestVal >= beta:
break # beta cutoff, a>=b
else:
bestVal = self.INF
for n in node['children']:
bestVal = min(
bestVal,
self.alphaBetaSearchDijkstra(n, depth - 1, alpha, beta,
True))
beta = min(beta, bestVal) # Updating beta
if alpha >= bestVal:
break # alpha cutoff, a>=b
node['value'] = bestVal
return bestVal
from hex_skeleton import HexBoard
import copy
import numpy as np
# global vars
_board_size: int = 11
_INF: float = 99999.0
# initialize board with size n
_board = HexBoard(_board_size)
def _update_board(board: HexBoard, l_move, is_max: bool) -> HexBoard:
board = copy.deepcopy(
board
) # I think this was the problem with the minimax core, it was using a reference instead of a deep copy
color = board.BLUE if is_max else board.RED
board.place(l_move, color)
return board
def dummy_eval() -> float:
return np.random.randint(0, 10)
def simple_dijkstra(board: HexBoard, source, is_max):
Q = set()
V_set = board.get_all_vertices()
dist = {}