def test_policy_zero_is_uniform(self, linear_averaging,
regret_matching_plus, alternating_updates):
# We use Leduc and not Kuhn, because Leduc has illegal actions and Kuhn does
# not.
game = pyspiel.load_game("leduc_poker")
cfr_solver = cfr._CFRSolver(game,
regret_matching_plus=regret_matching_plus,
linear_averaging=linear_averaging,
alternating_updates=alternating_updates)
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)
def test_cfr_kuhn_poker_runs_with_multiple_players(self, linear_averaging,
regret_matching_plus,
alternating_updates):
num_players = 3
game = pyspiel.load_game("kuhn_poker", {"players": num_players})
cfr_solver = cfr._CFRSolver(game,
regret_matching_plus=regret_matching_plus,
linear_averaging=linear_averaging,
alternating_updates=alternating_updates)
for _ in range(10):
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] * num_players)
del average_policy_values
def test_simultaneous_two_step_avg_1b_seq_in_kuhn_poker(
self, regret_matching_plus, initialize_cumulative_values):
num_players = 2
game = pyspiel.load_game(
"kuhn_poker", {"players": pyspiel.GameParameter(num_players)})
cfr_solver = cfr._CFRSolver(
game,
initialize_cumulative_values=initialize_cumulative_values,
regret_matching_plus=regret_matching_plus,
linear_averaging=False,
alternating_updates=False)
def check_avg_policy_is_uniform_random():
policy = cfr_solver.average_policy()
for player_info_states in policy.states_per_player:
for info_state in player_info_states:
state_policy = policy.policy_for_key(info_state)
np.testing.assert_allclose(state_policy,
[1.0 / len(state_policy)] *
len(state_policy))
check_avg_policy_is_uniform_random()
cfr_solver.evaluate_and_update_policy()
check_avg_policy_is_uniform_random()
cfr_solver.evaluate_and_update_policy()
# The acting player in 1b is player 1 and they have not acted before, so
# the probability this player plays to this information state is 1, and
# the sequence probability of any action is just the probability of that
# action given the information state. On the first iteration, this
# probability is 0.5 for both actions. On the second iteration, the
# current policy is [0, 1], so the average cumulants should be
# [0.5, 1.5]. Normalizing this gives the average policy.
normalization = 0.5 + 0.5 + 1
np.testing.assert_allclose(
cfr_solver.average_policy().policy_for_key("1b"),
[0.5 / normalization, (0.5 + 1) / normalization])
