def test_cfr_cce_dist_goofspiel(self):
"""Copy of the TestCCEDistCFRGoofSpiel in corr_dist_test.cc."""
game = pyspiel.load_game(
"turn_based_simultaneous_game(game=goofspiel(num_cards=3,points_order="
"descending,returns_type=total_points))")
for num_iterations in [1, 10, 100]:
policies = []
cfr_solver = cfr.CFRSolver(game)
for _ in range(num_iterations):
cfr_solver.evaluate_and_update_policy()
policies.append(
policy.python_policy_to_pyspiel_policy(
cfr_solver.current_policy()))
mu = pyspiel.uniform_correlation_device(policies)
cce_dist1 = pyspiel.cce_dist(game, mu)
print(
"goofspiel, cce test num_iterations: {}, cce_dist: {}".format(
num_iterations, cce_dist1))
# Assemble the same correlation device manually, just as an example for
# how to do non-uniform distributions of them and to test the python
# bindings for lists of tuples works properly
uniform_prob = 1.0 / len(policies)
mu2 = [(uniform_prob, policy) for policy in policies]
cce_dist2 = pyspiel.cce_dist(game, mu2)
self.assertAlmostEqual(cce_dist1, cce_dist2)
def test_cfr_cce_ce_dist_goofspiel(self):
"""Copy of the TestCCEDistCFRGoofSpiel in corr_dist_test.cc."""
game = pyspiel.load_game(
"turn_based_simultaneous_game(game=goofspiel(num_cards=3,points_order="
"descending,returns_type=total_points))")
for num_iterations in [1, 10, 100]:
policies = []
cfr_solver = cfr.CFRSolver(game)
for _ in range(num_iterations):
cfr_solver.evaluate_and_update_policy()
policies.append(
policy.python_policy_to_pyspiel_policy(
cfr_solver.current_policy()))
mu = pyspiel.uniform_correlation_device(policies)
cce_dist_info = pyspiel.cce_dist(game, mu)
print(
"goofspiel, cce test num_iters: {}, cce_dist: {}, per player: {}"
.format(num_iterations, cce_dist_info.dist_value,
cce_dist_info.deviation_incentives))
# Try converting one of the BR policies:
_ = policy.pyspiel_policy_to_python_policy(
game, cce_dist_info.best_response_policies[0])
# Assemble the same correlation device manually, just as an example for
# how to do non-uniform distributions of them and to test the python
# bindings for lists of tuples works properly
uniform_prob = 1.0 / len(policies)
mu2 = [(uniform_prob, policy) for policy in policies]
cce_dist_info2 = pyspiel.cce_dist(game, mu2)
self.assertAlmostEqual(cce_dist_info2.dist_value,
sum(cce_dist_info.deviation_incentives))
# Test the CEDist function too, why not. Disable the exact one, as it
# takes too long for a test.
# ce_dist_info = pyspiel.ce_dist(game, pyspiel.determinize_corr_dev(mu))
ce_dist_info = pyspiel.ce_dist(
game, pyspiel.sampled_determinize_corr_dev(mu, 100))
print(
"goofspiel, ce test num_iters: {}, ce_dist: {}, per player: {}"
.format(num_iterations, ce_dist_info.dist_value,
ce_dist_info.deviation_incentives))
print("number of conditional best responses per player:")
for p in range(game.num_players()):
print(" player {}, num: {}".format(
p,
len(ce_dist_info.conditional_best_response_policies[p])))
def find_best_response(
game, meta_dist, meta_game, iteration, joint_policies,
target_equilibrium, update_players_strategy):
"""Returns new best response policies."""
num_players = meta_game.shape[0]
per_player_num_policies = meta_dist.shape[:]
# Player update strategy.
if update_players_strategy == "all":
players = list(range(num_players))
elif update_players_strategy == "cycle":
players = [iteration % num_players]
elif update_players_strategy == "random":
players = [np.random.randint(0, num_players)]
else:
raise ValueError(
"update_players_strategy must be a valid player update strategy: "
"%s. Received: %s" % (UPDATE_PLAYERS_STRATEGY, update_players_strategy))
# Find best response.
per_player_new_policies = []
per_player_deviation_incentives = []
if target_equilibrium == "cce":
for player in range(num_players):
if player in players:
joint_policy_ids = itertools.product(*[
(np_-1,) if p_ == player else range(np_) for p_, np_
in enumerate(per_player_num_policies)])
joint_policies_slice = [
joint_policies[jpid] for jpid in joint_policy_ids]
meta_dist_slice = np.sum(meta_dist, axis=player)
meta_dist_slice[meta_dist_slice < DIST_TOL] = 0.0
meta_dist_slice[meta_dist_slice > 1.0] = 1.0
meta_dist_slice /= np.sum(meta_dist_slice)
meta_dist_slice = meta_dist_slice.flat
mu = [(p, mp) for mp, p in zip(joint_policies_slice, meta_dist_slice)
if p > 0]
info = pyspiel.cce_dist(game, mu, player, prob_cut_threshold=0.0)
new_policy = policy.pyspiel_policy_to_python_policy(
game, info.best_response_policies[0], players=(player,))
on_policy_value = np.sum(meta_game[player] * meta_dist)
deviation_incentive = max(
info.best_response_values[0] - on_policy_value, 0)
if deviation_incentive < GAP_TOL:
deviation_incentive = 0.0
per_player_new_policies.append([new_policy])
per_player_deviation_incentives.append([deviation_incentive])
else:
per_player_new_policies.append([])
per_player_deviation_incentives.append([])
elif target_equilibrium == "ce":
for player in range(num_players):
if player in players:
per_player_new_policies.append([])
per_player_deviation_incentives.append([])
for pid in range(per_player_num_policies[player]):
joint_policy_ids = itertools.product(*[
(pid,) if p_ == player else range(np_) for p_, np_
in enumerate(per_player_num_policies)])
joint_policies_slice = [
joint_policies[jpid] for jpid in joint_policy_ids]
inds = tuple((pid,) if player == p_ else slice(None)
for p_ in range(num_players))
meta_dist_slice = np.ravel(meta_dist[inds]).copy()
meta_dist_slice[meta_dist_slice < DIST_TOL] = 0.0
meta_dist_slice[meta_dist_slice > 1.0] = 1.0
meta_dist_slice_sum = np.sum(meta_dist_slice)
if meta_dist_slice_sum > 0.0:
meta_dist_slice /= meta_dist_slice_sum
mu = [(p, mp) for mp, p in
zip(joint_policies_slice, meta_dist_slice)
if p > 0]
info = pyspiel.cce_dist(game, mu, player, prob_cut_threshold=0.0)
new_policy = policy.pyspiel_policy_to_python_policy(
game, info.best_response_policies[0], players=(player,))
on_policy_value = np.sum(
np.ravel(meta_game[player][inds]) * meta_dist_slice)
deviation_incentive = max(
info.best_response_values[0] - on_policy_value, 0)
if deviation_incentive < GAP_TOL:
deviation_incentive = 0.0
per_player_new_policies[-1].append(new_policy)
per_player_deviation_incentives[-1].append(
meta_dist_slice_sum * deviation_incentive)
else:
per_player_new_policies.append([])
per_player_deviation_incentives.append([])
else:
raise ValueError(
"target_equilibrium must be a valid best response strategy: %s. "
"Received: %s" % (BRS, target_equilibrium))
return per_player_new_policies, per_player_deviation_incentives