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)))
import numpy as np
import collections
def train_anet(anet, RBUF):
# Creates a minibatch of the RBUF and trains the anet on the minibatch
batch_size = min(len(RBUF), 32)
minibatch = random.sample(RBUF, batch_size)
anet.train(minibatch)
""" Initializations """
anet = ANET(size=board_size)
agent = MCTS(exploration_rate=1, anet=anet)
sm = StateManager()
game = sm.create_game()
tree = Tree(game)
win_stats = []
RBUF = collections.deque(maxlen=rbuf_max_size)
for i in range(offset, num_of_games + 1):
print("Episode: {}/{}".format(i, num_of_games))
state = tree.root
while (not sm.is_game_over()):
best_child, training_case = agent.uct_search(tree, state,
num_search_games)
RBUF.append(training_case)
sm.move(best_child.move)
state = best_child
if verbose and i == num_of_games:
