def test_outcome_sampling_kuhn_3p(self):
np.random.seed(SEED)
game = pyspiel.load_game("kuhn_poker", {"players": 3})
os_solver = outcome_sampling_mccfr.OutcomeSamplingSolver(game)
for _ in range(10000):
os_solver.iteration()
conv = exploitability.nash_conv(game, os_solver.average_policy())
print("Kuhn3P, conv = {}".format(conv))
self.assertLess(conv, 0.22)
def test_int_mccfr_on_turn_based_game_with_exploitability(self):
"""Check if outcome sampling MCCFR can be applied."""
game = pyspiel.load_game(
"python_dynamic_routing(max_num_time_step=5,time_step_length=1.0)")
seq_game = pyspiel.convert_to_turn_based(game)
cfr_solver = outcome_mccfr.OutcomeSamplingSolver(seq_game)
for _ in range(_NUM_ITERATION_CFR_TEST):
cfr_solver.iteration()
exploitability.nash_conv(seq_game, cfr_solver.average_policy())
def test_outcome_sampling_leduc_2p(self):
np.random.seed(SEED)
game = pyspiel.load_game("leduc_poker")
os_solver = outcome_sampling_mccfr.OutcomeSamplingSolver(game)
for _ in range(10000):
os_solver.iteration()
conv = exploitability.nash_conv(game, os_solver.average_policy())
print("Leduc2P, conv = {}".format(conv))
self.assertLess(conv, 3.07)
def test_outcome_sampling_kuhn_2p(self):
np.random.seed(SEED)
game = pyspiel.load_game("kuhn_poker")
os_solver = outcome_sampling_mccfr.OutcomeSamplingSolver(game)
for _ in range(1000):
os_solver.iteration()
conv = exploitability.nash_conv(
game,
policy.PolicyFromCallable(game, os_solver.callable_avg_policy()))
print("Kuhn2P, conv = {}".format(conv))
self.assertGreater(conv, 0.2)
self.assertLess(conv, 0.3)
def main(_):
game = pyspiel.load_game(FLAGS.game, {"players": FLAGS.players})
if FLAGS.sampling == "external":
cfr_solver = external_mccfr.ExternalSamplingSolver(
game, external_mccfr.AverageType.SIMPLE)
else:
cfr_solver = outcome_mccfr.OutcomeSamplingSolver(game)
for i in range(FLAGS.iterations):
cfr_solver.iteration()
if i % FLAGS.print_freq == 0:
conv = exploitability.nash_conv(game, cfr_solver.average_policy())
print("Iteration {} exploitability {}".format(i, conv))
def test_outcome_sampling_leduc_2p(self):
np.random.seed(SEED)
game = pyspiel.load_game("leduc_poker")
os_solver = outcome_sampling_mccfr.OutcomeSamplingSolver(game)
for _ in range(1000):
os_solver.iteration()
conv = exploitability.nash_conv(
game,
policy.tabular_policy_from_callable(game,
os_solver.callable_avg_policy()))
print("Leduc2P, conv = {}".format(conv))
self.assertGreater(conv, 4.5)
self.assertLess(conv, 4.6)
def test_outcome_sampling_kuhn_2p(self):
np.random.seed(SEED)
game = pyspiel.load_game("kuhn_poker")
os_solver = outcome_sampling_mccfr.OutcomeSamplingSolver(game)
for _ in range(10000):
os_solver.iteration()
conv = exploitability.nash_conv(game, os_solver.average_policy())
print("Kuhn2P, conv = {}".format(conv))
self.assertLess(conv, 0.17)
# ensure that to_tabular() works on the returned policy
# and the tabular policy is equivalent
tabular_policy = os_solver.average_policy().to_tabular()
conv2 = exploitability.nash_conv(game, tabular_policy)
self.assertEqual(conv, conv2)
def test_outcome_sampling_kuhn_3p(self):
np.random.seed(SEED)
game = pyspiel.load_game("kuhn_poker",
{"players": pyspiel.GameParameter(3)})
os_solver = outcome_sampling_mccfr.OutcomeSamplingSolver(game)
for _ in range(1000):
os_solver.iteration()
conv = exploitability.nash_conv(
game,
policy.tabular_policy_from_callable(game,
os_solver.callable_avg_policy()))
print("Kuhn3P, conv = {}".format(conv))
self.assertGreater(conv, 0.3)
self.assertLess(conv, 0.4)
