def main(_):
games_list = pyspiel.registered_games()
print("Registered games:")
print(games_list)
# Load a two-player normal-form game as a two-player matrix game.
blotto_matrix_game = pyspiel.load_matrix_game("blotto")
print(
"Number of rows in 2-player Blotto with default settings is {}".format(
blotto_matrix_game.num_rows()))
# Several ways to load/create the same game of matching pennies.
print("Creating matrix game...")
game = pyspiel.load_matrix_game("matrix_mp")
game = _manually_create_game()
game = _import_data_create_game()
game = _easy_create_game()
game = _even_easier_create_game()
# Quick test: inspect top-left utility values:
print("Values for joint action ({},{}) is {},{}".format(
game.row_action_name(0), game.col_action_name(0),
game.player_utility(0, 0, 0), game.player_utility(1, 0, 0)))
state = game.new_initial_state()
# Print the initial state
print("State:")
print(str(state))
assert state.is_simultaneous_node()
# Simultaneous node: sample actions for all players.
chosen_actions = [
random.choice(state.legal_actions(pid))
for pid in range(game.num_players())
]
print("Chosen actions: ", [
state.action_to_string(pid, action)
for pid, action in enumerate(chosen_actions)
])
state.apply_actions(chosen_actions)
assert state.is_terminal()
# Game is now done. Print utilities for each player
returns = state.returns()
for pid in range(game.num_players()):
print("Utility for player {} is {}".format(pid, returns[pid]))
def test_constant_sum_transition_matrix(self):
"""Tests closed-form transition matrix computation for constant-sum case."""
game = pyspiel.load_matrix_game("matrix_rps")
payoff_tables = utils.nfg_to_ndarray(game)
# Checks if the game is symmetric and runs single-population analysis if so
_, payoff_tables = utils.is_symmetric_matrix_game(payoff_tables)
payoffs_are_hpt_format = utils.check_payoffs_are_hpt(payoff_tables)
m = 20
alpha = 0.1
# Case 1) General-sum game computation (slower)
game_is_constant_sum = False
use_local_selection_model = False
payoff_sum = None
c1, rhos1 = alpharank._get_singlepop_transition_matrix(
payoff_tables[0], payoffs_are_hpt_format, m, alpha,
game_is_constant_sum, use_local_selection_model, payoff_sum)
# Case 2) Constant-sum closed-form computation (faster)
game_is_constant_sum, payoff_sum = utils.check_is_constant_sum(
payoff_tables[0], payoffs_are_hpt_format)
c2, rhos2 = alpharank._get_singlepop_transition_matrix(
payoff_tables[0], payoffs_are_hpt_format, m, alpha,
game_is_constant_sum, use_local_selection_model, payoff_sum)
# Ensure both cases match
np.testing.assert_array_almost_equal(c1, c2)
np.testing.assert_array_almost_equal(rhos1, rhos2)
def test_single_step(self):
game = pyspiel.load_matrix_game("matrix_rps")
solver = double_oracle.DoubleOracleSolver(game)
solver.subgame_strategies = [[0], [0]]
best_response, best_response_utility = solver.step()
self.assertListEqual(best_response, [1, 1])
self.assertListEqual(best_response_utility, [1.0, 1.0])
def test_plot_pi_vs_alpha(self, mock_plt):
# Construct game
game = pyspiel.load_matrix_game("matrix_rps")
payoff_tables = utils.game_payoffs_array(game)
_, payoff_tables = utils.is_symmetric_matrix_game(payoff_tables)
payoffs_are_hpt_format = utils.check_payoffs_are_hpt(payoff_tables)
# Compute alpharank
alpha = 1e2
_, _, pi, num_profiles, num_strats_per_population = (
alpharank.compute(payoff_tables, alpha=alpha))
strat_labels = utils.get_strat_profile_labels(payoff_tables,
payoffs_are_hpt_format)
num_populations = len(payoff_tables)
# Construct synthetic pi-vs-alpha history
pi_list = np.empty((num_profiles, 0))
alpha_list = []
for _ in range(2):
pi_list = np.append(pi_list, np.reshape(pi, (-1, 1)), axis=1)
alpha_list.append(alpha)
# Test plotting code (via pyplot mocking to prevent plot pop-up)
alpharank_visualizer.plot_pi_vs_alpha(
pi_list.T,
alpha_list,
num_populations,
num_strats_per_population,
strat_labels,
num_strats_to_label=0)
self.assertTrue(mock_plt.show.called)
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_nfg_to_ndarray_pd(self):
"""Test `nfg_to_ndarray` for prisoners' dilemma."""
game = pyspiel.load_matrix_game("matrix_pd")
payoff_matrix = np.empty(shape=(2, 2, 2))
payoff_row = np.array([[5., 0.], [10., 1.]])
payoff_matrix[0] = payoff_row
payoff_matrix[1] = payoff_row.T
np.testing.assert_allclose(utils.nfg_to_ndarray(game), payoff_matrix)
def test_multi_population_rps(self):
game = pyspiel.load_matrix_game('matrix_rps')
payoff_matrix = game_payoffs_array(game)
rd = dynamics.replicator
dyn = dynamics.MultiPopulationDynamics(payoff_matrix, [rd] * 2)
x = np.concatenate(
[np.ones(k) / float(k) for k in payoff_matrix.shape[1:]])
np.testing.assert_allclose(dyn(x), np.zeros((6, )), atol=1e-15)
def test_nfg_to_ndarray_rps(self):
"""Test `nfg_to_ndarray` for rock-paper-scissors."""
game = pyspiel.load_matrix_game("matrix_rps")
payoff_matrix = np.empty(shape=(2, 3, 3))
payoff_row = np.array([[0., -1., 1.], [1., 0., -1.], [-1., 1., 0.]])
payoff_matrix[0] = payoff_row
payoff_matrix[1] = -1. * payoff_row
np.testing.assert_allclose(utils.nfg_to_ndarray(game), payoff_matrix)
def test_rock_paper_scissors(self):
game = pyspiel.load_matrix_game("matrix_rps")
solver = double_oracle.DoubleOracleSolver(game)
solution, iteration, value = solver.solve(
initial_strategies=[[0], [0]])
np.testing.assert_allclose(solution[0], np.ones(3) / 3.)
np.testing.assert_allclose(solution[1], np.ones(3) / 3.)
self.assertEqual(iteration, 3)
self.assertAlmostEqual(value, 0.0)
def test_rd_rps_pure_fixed_points(self):
game = pyspiel.load_matrix_game('matrix_rps')
payoff_matrix = game_payoffs_array(game)
rd = dynamics.replicator
dyn = dynamics.SinglePopulationDynamics(payoff_matrix, rd)
x = np.eye(3)
np.testing.assert_allclose(dyn(x[0]), np.zeros((3, )))
np.testing.assert_allclose(dyn(x[1]), np.zeros((3, )))
np.testing.assert_allclose(dyn(x[2]), np.zeros((3, )))
def test_solve_blotto(self):
blotto_matrix_game = pyspiel.load_matrix_game("blotto")
p0_sol, p1_sol, p0_sol_val, p1_sol_val = (
lp_solver.solve_zero_sum_matrix_game(blotto_matrix_game))
self.assertEqual(len(p0_sol), blotto_matrix_game.num_rows())
self.assertEqual(len(p1_sol), blotto_matrix_game.num_cols())
# Symmetric game, must be zero
self.assertAlmostEqual(p0_sol_val, 0.0)
self.assertAlmostEqual(p1_sol_val, 0.0)
def test_constant_sum_checker(self):
"""Tests if verification of constant-sum game is correct."""
game = pyspiel.load_matrix_game("matrix_rps")
payoff_tables = utils.nfg_to_ndarray(game)
payoffs_are_hpt_format = utils.check_payoffs_are_hpt(payoff_tables)
game_is_constant_sum, payoff_sum = utils.check_is_constant_sum(
payoff_tables[0], payoffs_are_hpt_format)
self.assertTrue(game_is_constant_sum)
self.assertEqual(payoff_sum, 0.)
def test_extensive_to_matrix_game_payoff_matrix(self):
turn_based_game = pyspiel.load_game_as_turn_based("matrix_pd")
matrix_game = pyspiel.extensive_to_matrix_game(turn_based_game)
orig_game = pyspiel.load_matrix_game("matrix_pd")
for row in range(orig_game.num_rows()):
for col in range(orig_game.num_cols()):
for player in range(2):
self.assertEqual(
orig_game.player_utility(player, row, col),
matrix_game.player_utility(player, row, col))
def simulate_dynamics(args):
game = pyspiel.load_matrix_game('matrix_' + args.game)
payoff_matrix = game_payoffs_array(game)
game_dim = len(payoff_matrix[0])
print(payoff_matrix)
func = getattr(algos, args.algo)
if game_dim == 3:
dyn = build_3x3_dynamics(payoff_matrix, func)
else:
dyn = build_2x2_dynamics(payoff_matrix, func)
return game_dim, dyn
def test_iterated_dominance_prisoners_dilemma(self):
# find the strictly dominant (D, D) strategy
pd = pyspiel.load_matrix_game("matrix_pd")
pd_dom, pd_live = self._checked_iterated_dominance(
pd, lp_solver.DOMINANCE_STRICT)
self.assertEqual(pd_dom.num_rows(), 1)
self.assertEqual(pd_dom.num_cols(), 1)
self.assertEqual(pd_dom.row_action_name(0), "Defect")
self.assertEqual(pd_dom.col_action_name(0), "Defect")
self.assertListEqual(pd_live[0].tolist(), [False, True])
self.assertListEqual(pd_live[1].tolist(), [False, True])
def test_dominance_prisoners_dilemma(self):
self._assert_dominated(0, pyspiel.load_matrix_game("matrix_pd"), 1,
lp_solver.DOMINANCE_STRICT)
self._assert_undominated(1, pyspiel.load_matrix_game("matrix_pd"), 1,
lp_solver.DOMINANCE_VERY_WEAK)
def test_dynamics_rps_mixed_fixed_point(self, func):
game = pyspiel.load_matrix_game('matrix_rps')
payoff_matrix = game_payoffs_array(game)
dyn = dynamics.SinglePopulationDynamics(payoff_matrix, func)
x = np.ones(shape=(3, )) / 3.
np.testing.assert_allclose(dyn(x), np.zeros((3, )), atol=1e-15)
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())
