def get_alpha_rank_pi(payoff_table):
# matrix must be symmetric
assert len(np.shape(payoff_table)) == 2
assert np.shape(payoff_table)[0] == np.shape(payoff_table)[1]
payoff_tables = (payoff_table, payoff_table.T)
payoff_tables = [
heuristic_payoff_table.from_matrix_game(payoff_tables[0]),
heuristic_payoff_table.from_matrix_game(payoff_tables[1].T)
]
# Check if the game is symmetric (i.e., players have identical strategy sets
# and payoff tables) and return only a single-player’s payoff table if so.
# This ensures Alpha-Rank automatically computes rankings based on the
# single-population dynamics.
is_symmetric, payoff_tables = utils.is_symmetric_matrix_game(payoff_tables)
assert is_symmetric
# Compute Alpha-Rank
(rhos, rho_m, pi, num_profiles,
num_strats_per_population) = alpharank.compute(payoff_tables, alpha=1e2)
for i in range(len(pi)):
if np.isclose(pi[i], 0.0):
pi[i] = 0.0
return pi
def test_stationary_distribution(self):
"""Tests stationary distribution using payoffs from Han et al., 2013."""
r = 1.
t = 2.
p = 0.
s = -1.
delta = 4.
eps = 0.25
payoff_tables = [
np.asarray([[r - eps / 2., r - eps, 0, s + delta - eps, r - eps],
[r, r, s, s, s], [0, t, p, p, p],
[t - delta, t, p, p, p], [r, t, p, p, p]])
]
m = 20
alpha = 0.1
expected_pi = np.asarray(
[0.40966787, 0.07959841, 0.20506998, 0.08505983, 0.2206039])
# Test payoffs in matrix format
_, _, pi_matrix, _, _ = alpharank.compute(
payoff_tables, m=m, alpha=alpha, use_local_selection_model=False)
np.testing.assert_array_almost_equal(pi_matrix, expected_pi, decimal=4)
# Test payoffs in HPT format
hpts = [heuristic_payoff_table.from_matrix_game(payoff_tables[0])]
_, _, pi_hpts, _, _ = alpharank.compute(
hpts, m=m, alpha=alpha, use_local_selection_model=False)
np.testing.assert_array_almost_equal(pi_hpts, expected_pi, decimal=4)
def test_expected_payoff(self, strategy):
logging.info("Testing expected payoff for matrix game.")
game = pyspiel.load_matrix_game("matrix_rps")
payoff_tables = utils.game_payoffs_array(game)
table = heuristic_payoff_table.from_matrix_game(payoff_tables[0])
expected_payoff = table.expected_payoff(strategy)
print(expected_payoff)
assert len(expected_payoff) == table._num_strategies
def test_from_matrix_game(self, game):
game = pyspiel.load_matrix_game(game)
payoff_tables = utils.game_payoffs_array(game)
logging.info("Testing payoff table construction for matrix game.")
table = heuristic_payoff_table.from_matrix_game(payoff_tables[0])
print(table())
