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 policy_bots():
random_policy = policy.UniformRandomPolicy(GAME)
py_bot = PolicyBot(0, np.random.RandomState(4321), random_policy)
cpp_bot = pyspiel.make_policy_bot(
GAME, 1, 1234,
policy.python_policy_to_pyspiel_policy(random_policy.to_tabular()))
return [py_bot, cpp_bot]
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 test_record_batched_trajectories(self):
for game_name in ["kuhn_poker", "leduc_poker", "liars_dice"]:
game = pyspiel.load_game(game_name)
python_policy = policy.TabularPolicy(game)
tabular_policy = policy.python_policy_to_pyspiel_policy(python_policy)
policies = [tabular_policy] * 2
# We test that we can create a batch of trajectories.
seed = 0
batch_size = 128
include_full_observations = False
pyspiel.record_batched_trajectories(game, policies,
python_policy.state_lookup,
batch_size, include_full_observations,
seed, -1)
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 test_matching_pennies_3p(self):
game = pyspiel.load_game_as_turn_based('matching_pennies_3p')
deep_cfr_solver = deep_cfr.DeepCFRSolver(game,
policy_network_layers=(16, 8),
advantage_network_layers=(32,
16),
num_iterations=2,
num_traversals=2,
learning_rate=1e-3,
batch_size_advantage=None,
batch_size_strategy=None,
memory_capacity=1e7)
deep_cfr_solver.solve()
conv = pyspiel.nash_conv(
game,
policy.python_policy_to_pyspiel_policy(
policy.tabular_policy_from_callable(
game, deep_cfr_solver.action_probabilities)))
logging.info('Deep CFR in Matching Pennies 3p. NashConv: %.2f', conv)
def main(unused_argv):
logging.info("Loading %s", FLAGS.game_name)
game = pyspiel.load_game(FLAGS.game_name)
deep_cfr_solver = deep_cfr.DeepCFRSolver(
game,
policy_network_layers=(32, 32),
advantage_network_layers=(16, 16),
num_iterations=FLAGS.num_iterations,
num_traversals=FLAGS.num_traversals,
learning_rate=1e-3,
batch_size_advantage=None,
batch_size_strategy=None,
memory_capacity=int(1e7))
_, advantage_losses, policy_loss = deep_cfr_solver.solve()
for player, losses in six.iteritems(advantage_losses):
logging.info("Advantage for player %d: %s", player,
losses[:2] + ["..."] + losses[-2:])
logging.info("Advantage Buffer Size for player %s: '%s'", player,
len(deep_cfr_solver.advantage_buffers[player]))
logging.info("Strategy Buffer Size: '%s'",
len(deep_cfr_solver.strategy_buffer))
logging.info("Final policy loss: '%s'", policy_loss)
average_policy = policy.tabular_policy_from_callable(
game, deep_cfr_solver.action_probabilities)
pyspiel_policy = policy.python_policy_to_pyspiel_policy(average_policy)
conv = pyspiel.nash_conv(game, pyspiel_policy)
logging.info("Deep CFR in '%s' - NashConv: %s", FLAGS.game_name, conv)
average_policy_values = expected_game_score.policy_value(
game.new_initial_state(), [average_policy] * 2)
logging.info("Computed player 0 value: %.2f (expected: %.2f).",
average_policy_values[0], -1 / 18)
logging.info("Computed player 1 value: %.2f (expected: %.2f).",
average_policy_values[1], 1 / 18)
def test_can_create_cpp_tabular_policy(self):
for game_name in ["kuhn_poker", "leduc_poker", "liars_dice"]:
game = pyspiel.load_game(game_name)
# We just test that we can create a tabular policy.
policy.python_policy_to_pyspiel_policy(policy.TabularPolicy(game))
def add_new_policies(
per_player_new_policies,
per_player_gaps,
per_player_repeats,
per_player_policies,
joint_policies,
joint_returns,
game,
br_selection):
"""Adds novel policies from new policies."""
num_players = len(per_player_new_policies)
per_player_num_novel_policies = [0 for _ in range(num_players)]
# Update policies and policy counts.
for player in range(num_players):
new_policies = per_player_new_policies[player]
new_gaps = per_player_gaps[player]
repeat_policies = []
repeat_gaps = []
repeat_ids = []
novel_policies = []
novel_gaps = []
for new_policy, new_gap in zip(new_policies, new_gaps):
for policy_id, policy_ in enumerate(per_player_policies[player]):
if np.all( # New policy is not novel.
new_policy.action_probability_array ==
policy_.action_probability_array):
logging.debug("Player %d's new policy is not novel.", player)
repeat_policies.append(new_policy)
repeat_gaps.append(new_gap)
repeat_ids.append(policy_id)
break
else: # New policy is novel.
logging.debug("Player %d's new policy is novel.", player)
novel_policies.append(new_policy)
novel_gaps.append(new_gap)
add_novel_policies = []
add_repeat_ids = []
if (novel_policies or repeat_policies):
if br_selection == "all":
add_novel_policies.extend(novel_policies)
add_repeat_ids.extend(repeat_ids)
elif br_selection == "all_novel":
add_novel_policies.extend(novel_policies)
elif br_selection == "random":
index = np.random.randint(0, len(repeat_policies) + len(novel_policies))
if index < len(novel_policies):
add_novel_policies.append(novel_policies[index])
else:
add_repeat_ids.append(repeat_ids[index - len(novel_policies)])
elif br_selection == "random_novel":
if novel_policies:
index = np.random.randint(0, len(novel_policies))
add_novel_policies.append(novel_policies[index])
else: # Fall back on random.
index = np.random.randint(0, len(repeat_policies))
add_repeat_ids.append(repeat_ids[index])
elif br_selection == "largest_gap":
if novel_policies:
index = np.argmax(novel_gaps)
if novel_gaps[index] == 0.0: # Fall back to random when zero.
index = np.random.randint(0, len(novel_policies))
add_novel_policies.append(novel_policies[index])
else: # Fall back on random.
index = np.random.randint(0, len(repeat_policies))
add_repeat_ids.append(repeat_ids[index])
else:
raise ValueError("Unrecognized br_selection method: %s"
% br_selection)
for add_repeat_id in add_repeat_ids:
per_player_repeats[player][add_repeat_id] += 1
for add_novel_policy in add_novel_policies:
per_player_policies[player].append(add_novel_policy) # Add new policy.
per_player_repeats[player].append(1) # Add new count.
per_player_num_novel_policies[player] += 1
# Add new joint policies.
for pids in itertools.product(*[
range(len(policies)) for policies in per_player_policies]):
if pids in joint_policies:
continue
logging.debug("Evaluating novel joint policy: %s.", pids)
policies = [
policies[pid] for pid, policies in zip(pids, per_player_policies)]
python_tabular_policy = policy.merge_tabular_policies(
policies, game)
pyspiel_tabular_policy = policy.python_policy_to_pyspiel_policy(
python_tabular_policy)
joint_policies[pids] = pyspiel_tabular_policy
joint_returns[pids] = [
0.0 if abs(er) < RETURN_TOL else er
for er in pyspiel.expected_returns(
game.new_initial_state(), pyspiel_tabular_policy, -1, True)]
return per_player_num_novel_policies