def main():
network = load_model(args.newnetwork)
score_net = 0
score_random = 0
for game in range(args.numgames):
moves = 0
temperature = 10e-6
env = oz_env()
env.reset()
root_node = Node(env, Config.EXPLORE_FACTOR)
game_over = False
# print(root_node.env.board[71])
while not game_over:
start = time.time()
if root_node.env.board[71] == -1:
#print("am here")
pi, successor, root_node = MCTS(temp=temperature, network= network, root=root_node)
root_node = successor
else:
if (root_node.children == None):
root_node.children = [None]*len(root_node.legal_moves)
move = np.random.randint(0,(len(root_node.legal_moves)))
if (root_node.children[move] is None):
next_env = deepcopy(root_node.env)
next_env.step(root_node.legal_moves[move])
root_node.children[move] = Node(next_env,temperature,parent=root_node,child_id=move)
root_node = root_node.children[move]
moves = moves + 1
game_over = root_node.env.is_game_over()
z = root_node.env.who_won()
if z <= -1:
score_net += 1
else:
score_random += 1
print("Game {} complete. Net: {} Random: {}".format(game, score_net, score_random))
print("New network score total wins: {} Average Score: {}".format(score_net, score_net / args.numgames))
print("Random play score total wins: {} Average Score: {}".format(score_random, score_random / args.numgames))
def main():
#print("check-2")
network = load_model(args.newnetwork)
#print("am here")
score_net = 0
score_random = 0
for game in range(args.numgames):
moves = 0
temperature = 10e-6
white = 1
black = None
env = oz_env()
env.reset()
root_node = Node(env, Config.EXPLORE_FACTOR)
game_over = False
while not game_over:
if root_node.env.board[71] == 1:
player = white
else:
player = black
#print(root_node.env.board)
start = time.time()
if player == white:
#print(root_node.env.board)
print("am here-1\n")
pi, successor, root_node = MCTS(temp=temperature,
network=network,
root=root_node)
#print(root_node.env.board)
print("MCTS completed move {} in: {}".format(
moves,
time.time() - start))
root_node = successor
else:
print("am here-2\n")
if (root_node.children == None):
root_node.children = [None] * len(root_node.legal_moves)
move = np.random.randint(0, (len(root_node.legal_moves)))
if (root_node.children[move] is None):
#next_env = root_node.env.deepcopy()
next_env = deepcopy(root_node.env)
#next_env = root_node.env
next_env.step(root_node.legal_moves[move])
root_node.children[move] = Node(next_env,
temperature,
parent=root_node,
child_id=move)
root_node = root_node.children[move]
print(root_node.env.board)
moves = moves + 1
game_over = root_node.env.is_game_over()
z = root_node.env.who_won()
# from white perspective
#if white == player:
if z >= 1:
score_net += 1
else:
score_random += 1
#else:
# if z <= -1:
# score_net += 1
# else:
# score_random += 1
print("Game {} complete. Net: {} Random: {}".format(
game, score_net, score_random))
print("New network score total wins: {} Average Score: {}".format(
score_net, score_net / args.numgames))
print("Random play score total wins: {} Average Score: {}".format(
score_random, score_random / args.numgames))
