def CFR_BR_Solving(game, iterations, save_every=0, save_prefix='base'):
def save_cfr_br():
policy = cfr_solver.average_policy()
policy = dict(zip(policy.state_lookup, policy.action_probability_array))
policy_handler.save_to_tabular_policy(game, policy, "policies/CFRBR/{}/{}".format(save_prefix, it))
cfr_solver = cfr_br.CFRBRSolver(game)
for it in range(iterations + 1):
if save_every != 0 and it % save_every == 0: # order is important
save_cfr_br()
cfr_solver.evaluate_and_update_policy()
save_cfr_br()
def test_policy_and_average_policy(self):
game = pyspiel.load_game("kuhn_poker")
cfrbr_solver = cfr_br.CFRBRSolver(game)
for _ in range(300):
cfrbr_solver.evaluate_and_update_policy()
average_policy = cfrbr_solver.average_policy()
average_policy_values = expected_game_score.policy_value(
game.new_initial_state(), [average_policy] * 2)
# 1/18 is the Nash value. See https://en.wikipedia.org/wiki/Kuhn_poker
np.testing.assert_allclose(
average_policy_values, [-1 / 18, 1 / 18], atol=1e-3)
cfrbr_solver.current_policy()
def test_policy_zero_is_uniform(self, linear_averaging, regret_matching_plus):
game = pyspiel.load_game("leduc_poker")
cfr_solver = cfr_br.CFRBRSolver(
game,
regret_matching_plus=regret_matching_plus,
linear_averaging=linear_averaging)
np.testing.assert_array_equal(
_LEDUC_UNIFORM_POLICY.action_probability_array,
cfr_solver.current_policy().action_probability_array)
np.testing.assert_array_equal(
_LEDUC_UNIFORM_POLICY.action_probability_array,
cfr_solver.average_policy().action_probability_array)