def test_policy(self):
game = pyspiel.load_game("kuhn_poker")
solver = cfr.CFRPlusSolver(game)
policy = solver.policy()
self.assertLen(policy, 12)
for values in policy.values():
self.assertEqual({0: 0.5, 1: 0.5}, values)
def test_policy(self):
game = pyspiel.load_game("kuhn_poker")
solver = cfr.CFRPlusSolver(game)
tabular_policy = solver.policy()
self.assertLen(tabular_policy.state_lookup, 12)
for info_state_str in tabular_policy.state_lookup.keys():
np.testing.assert_equal(
np.asarray([0.5, 0.5]), tabular_policy.policy_for_key(info_state_str))
def test_cfr_plus_kuhn_poker(self):
game = pyspiel.load_game("kuhn_poker")
cfr_solver = cfr.CFRPlusSolver(game)
for _ in range(200):
cfr_solver.evaluate_and_update_policy()
average_policy = cfr_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)
def test_cfr_plus_solver_best_response_mdp(self):
game = pyspiel.load_game("kuhn_poker")
cfr_solver = cfr.CFRPlusSolver(game)
for _ in range(200):
cfr_solver.evaluate_and_update_policy()
average_policy = cfr_solver.average_policy()
pyspiel_avg_policy = policy.python_policy_to_pyspiel_policy(
average_policy)
br_computer = pyspiel.TabularBestResponseMDP(game, pyspiel_avg_policy)
br_info = br_computer.exploitability()
self.assertLessEqual(br_info.exploitability, 0.001)
def CFRPlus_Solving(game, iterations, save_every=0, save_prefix='base', alternating_updates = True, linear_averaging = True):
def save_cfrplus():
avg_policy = cfr_solver.average_policy()
avg_policy = dict(zip(avg_policy.state_lookup, avg_policy.action_probability_array))
policy_handler.save_to_tabular_policy(game, avg_policy, "policies/CFRPlus/{}/{}".format(save_prefix, it))
cfr_solver = cfr.CFRPlusSolver(game)
#cfr_solver = cfr._CFRSolver(game, regret_matching_plus=True, alternating_updates=alternating_updates, linear_averaging=linear_averaging)
for it in range(iterations + 1): # so that if you tell it to train 20K iterations, the last save isn't 19999
if save_every != 0 and it % save_every == 0: # order is important
save_cfrplus()
cfr_solver.evaluate_and_update_policy()
save_cfrplus()