def disabled_test_external_sampling_liars_dice_2p_simple(self):
np.random.seed(SEED)
game = pyspiel.load_game("liars_dice")
es_solver = external_sampling_mccfr.ExternalSamplingSolver(
game, external_sampling_mccfr.AverageType.SIMPLE)
for _ in range(1):
es_solver.iteration()
conv = exploitability.nash_conv(game, es_solver.average_policy())
print("Liar's dice, conv = {}".format(conv))
self.assertLess(conv, 2)
def test_external_sampling_kuhn_3p_simple(self):
np.random.seed(SEED)
game = pyspiel.load_game("kuhn_poker", {"players": 3})
es_solver = external_sampling_mccfr.ExternalSamplingSolver(
game, external_sampling_mccfr.AverageType.SIMPLE)
for _ in range(10):
es_solver.iteration()
conv = exploitability.nash_conv(game, es_solver.average_policy())
print("Kuhn3P, conv = {}".format(conv))
self.assertLess(conv, 2)
def test_ext_mccfr_on_turn_based_game_with_exploitability(self):
"""Check if external 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 = external_mccfr.ExternalSamplingSolver(
seq_game, external_mccfr.AverageType.SIMPLE)
for _ in range(_NUM_ITERATION_CFR_TEST):
cfr_solver.iteration()
exploitability.nash_conv(seq_game, cfr_solver.average_policy())
def test_external_sampling_kuhn_2p_full(self):
np.random.seed(SEED)
game = pyspiel.load_game("kuhn_poker")
es_solver = external_sampling_mccfr.ExternalSamplingSolver(
game, external_sampling_mccfr.AverageType.FULL)
for _ in range(10):
es_solver.iteration()
conv = exploitability.nash_conv(game, es_solver.average_policy())
print("Kuhn2P, conv = {}".format(conv))
self.assertLess(conv, 1)
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_external_sampling_kuhn_2p_simple(self):
np.random.seed(SEED)
game = pyspiel.load_game("kuhn_poker")
es_solver = external_sampling_mccfr.ExternalSamplingSolver(
game, external_sampling_mccfr.AverageType.SIMPLE)
for _ in range(10):
es_solver.iteration()
conv = exploitability.nash_conv(
game,
policy.tabular_policy_from_callable(game,
es_solver.callable_avg_policy()))
print("Kuhn2P, conv = {}".format(conv))
self.assertLess(conv, 1)
def test_external_sampling_kuhn_3p_full(self):
np.random.seed(SEED)
game = pyspiel.load_game("kuhn_poker",
{"players": pyspiel.GameParameter(3)})
es_solver = external_sampling_mccfr.ExternalSamplingSolver(
game, external_sampling_mccfr.AverageType.FULL)
for _ in range(10):
es_solver.iteration()
conv = exploitability.nash_conv(
game,
policy.tabular_policy_from_callable(game,
es_solver.callable_avg_policy()))
print("Kuhn3P, conv = {}".format(conv))
self.assertLess(conv, 2)
def test_external_sampling_leduc_2p_simple(self):
np.random.seed(SEED)
game = pyspiel.load_game("leduc_poker")
es_solver = external_sampling_mccfr.ExternalSamplingSolver(
game, external_sampling_mccfr.AverageType.SIMPLE)
for _ in range(10):
es_solver.iteration()
conv = exploitability.nash_conv(game, es_solver.average_policy())
print("Leduc2P, conv = {}".format(conv))
self.assertLess(conv, 5)
# ensure that to_tabular() works on the returned policy and
# the tabular policy is equivalent
tabular_policy = es_solver.average_policy().to_tabular()
conv2 = exploitability.nash_conv(game, tabular_policy)
self.assertEqual(conv, conv2)
def external_sampling_monte_carlo_counterfactual_regret_minimization(
seq_game, number_of_iterations, compute_metrics=False):
cfr_solver = external_mccfr.ExternalSamplingSolver(
seq_game, external_mccfr.AverageType.SIMPLE)
tick_time = time.time()
# print("CFRSolver initialized.")
for _ in range(number_of_iterations):
cfr_solver.iteration()
timing = time.time() - tick_time
# print("Finish.")
if compute_metrics:
nash_conv = exploitability.nash_conv(seq_game,
cfr_solver.average_policy())
return timing, cfr_solver.average_policy(), nash_conv
return timing, cfr_solver.average_policy()
