def nn_vs_nn_export_better_player():
player1 = NNAgent1(verbose = True)
player2 = NNAgent1(load_best=True)
stats = Statistic(player1, verbose=True)
while True:
bg = Backgammon()
bg.set_player_1(player1)
bg.set_player_2(player2)
winner = bg.play()
player1.add_reward(winner)
player2.add_reward(-1 * winner)
stats.add_win(winner)
if stats.nn_is_better() and stats.games_played % 100 == 0:
break
# only way to reach this point is if the current
# neural network is better than the BestNNAgent()
# ... at least I think so
# thus, we export the current as best
print("Congratulations, you brought the network one step closer")
print("to taking over the world (of backgammon)!!!")
player1.export_model(filename="nn_best_model")
def do_default():
"""
Play with a neural network against random
"""
player1 = get_agent_by_config_name('nn_pg_2', 'best')
player2 = get_agent_by_config_name('random', 'None')
player1.training = True
player2.training = True
stats = Statistic(player1, verbose=True)
# play games forever
while True:
bg = Backgammon()
bg.set_player_1(player1)
bg.set_player_2(player2)
winner = bg.play()
player1.add_reward(winner)
player2.add_reward(-winner)
# Reward the neural network agent
# player1.reward_player(winner)
stats.add_win(winner)
