def play_with_agents(self, agt1, agt2):
player_turn = 1
player1_wins = 0
for i in range(self.G):
print("[{}>{}]".format("-" * i, "." * (self.G - i - 1)), end="\r")
sm = StateManager(5)
agent = MCTS(exploration_rate=1, anet=agt1)
game = sm.create_game()
tree = Tree(game, chanceOfRandom=0.0)
state = tree.root
while not sm.is_finished():
if player_turn == 1:
agent.anet = agt1
best_child = agent.uct_search(tree, state,
num_search_games)
else:
agent.anet = agt2
best_child = agent.uct_search(tree, state,
num_search_games)
game.execute_move(best_child.move)
state = best_child
if sm.get_winner() == 1:
player1_wins += 1
print("{} won {}/{} against {}.".format(agt1.name, player1_wins,
self.G, agt2.name))
print(np.reshape(sm.game.board, (boardsize, boardsize)))
def play_series(self, agt1, agt2):
player1_wins = 0
for i in range(self.G):
print("[{}>{}]".format("-" * i, "." * (self.G - i - 1)), end="\r")
sm = StateManager(5)
while not sm.is_finished():
player = sm.game.get_current_player()
state = np.array(
[np.concatenate((player, sm.game.board), axis=None)])
if player == 1:
predictions = agt1.predict(state)[0]
else:
predictions = agt2.predict(state)[0]
legal_moves = sm.get_legal_moves()
if len(sm.game.executedMoves) <= 1:
best_move = random.choice(legal_moves)
else:
best_move = self.choose_best_move(predictions, legal_moves)
sm.execute_move(best_move)
if sm.get_winner() == 1:
player1_wins += 1
print("{} won {}/{} against {}.".format(agt1.name, player1_wins,
self.G, agt2.name))
print(np.reshape(sm.game.board, (boardsize, boardsize)))
print(sm.game.executedMoves)
return player1_wins
def rollout(self, leaf):
size = int((len(leaf.state) - 1) ** 0.5)
leaf_state, first_iteration = StateManager(size, leaf.state), True
while True:
if leaf_state.player1_won():
score = 1.0
return score
elif leaf_state.player2_won():
score = -1.0
return score
if leaf_state.is_finished():
print("No winner error")
quit()
if first_iteration:
possible_moves = leaf_state.get_moves()
move = possible_moves[random.randint(0, len(possible_moves) - 1)]
first_iteration = False
else:
move = leaf_state.convert_to_move(self.nn.get_action(leaf_state.string_representation()))
leaf_state.make_move(move)
""" Initializations """
anet = ANET(boardsize)
agent = MCTS(exploration_rate=1, anet=anet)
sm = StateManager(boardsize)
game = sm.create_game()
tree = Tree(game, 1.0)
win_stats = []
# TODO: Save interval for ANET parameters
RBUF = collections.deque(maxlen=500)
for i in range(1, num_of_games + 1):
progress_bar(i + 1)
state = tree.root
while (not sm.is_finished()):
player = sm.game.get_current_player()
best_child = agent.uct_search(tree, state, num_search_games)
distribution = get_distribution(best_child.parent)
RBUF.append((np.concatenate((player, best_child.game.board),
axis=None), distribution))
x_train, y_train = zip(*RBUF)
print("Move: ", best_child.move)
game.execute_move(best_child.move)
state = best_child
# Train ANET on a random minibatch from RBUF
train_anet(anet, RBUF)
if i % save_interval == 0:
anet.model.save('./trainedModels/model-{}.h5'.format(i))
