def test_braess_paradox(self):
"""Test that Braess paradox can be reproduced with the mean field game."""
mfg_game = pyspiel.load_game("python_mfg_dynamic_routing", {
"time_step_length": 0.05,
"max_num_time_step": 100
})
class NashEquilibriumBraess(policy.Policy):
def action_probabilities(self, state, player_id=None):
legal_actions = state.legal_actions()
if not legal_actions:
return {dynamic_routing_utils.NO_POSSIBLE_ACTION: 1.0}
elif len(legal_actions) == 1:
return {legal_actions[0]: 1.0}
else:
if legal_actions[0] == 2:
return {2: 0.75, 3: 0.25}
elif legal_actions[0] == 4:
return {4: 2 / 3, 5: 1 / 3}
raise ValueError(f"{legal_actions} is not correct.")
ne_policy = NashEquilibriumBraess(mfg_game, 1)
self.assertEqual(
-policy_value.PolicyValue(
mfg_game, distribution.DistributionPolicy(mfg_game, ne_policy),
ne_policy).value(mfg_game.new_initial_state()), 3.75)
self.assertEqual(
nash_conv.NashConv(mfg_game, ne_policy).nash_conv(), 0.0)
class SocialOptimumBraess(policy.Policy):
def action_probabilities(self, state, player_id=None):
legal_actions = state.legal_actions()
if not legal_actions:
return {dynamic_routing_utils.NO_POSSIBLE_ACTION: 1.0}
elif len(legal_actions) == 1:
return {legal_actions[0]: 1.0}
else:
if legal_actions[0] == 2:
return {2: 0.5, 3: 0.5}
elif legal_actions[0] == 4:
return {5: 1.0}
raise ValueError(f"{legal_actions} is not correct.")
so_policy = SocialOptimumBraess(mfg_game, 1)
self.assertEqual(
-policy_value.PolicyValue(
mfg_game, distribution.DistributionPolicy(mfg_game, so_policy),
so_policy).value(mfg_game.new_initial_state()), 3.5)
self.assertEqual(
nash_conv.NashConv(mfg_game, so_policy).nash_conv(), 0.75)
def iteration(self, rl_br_agent=None, learning_rate=None):
"""Returns a new `TabularPolicy` equivalent to this policy.
Args:
rl_br_agent: An instance of the RL approximation method to use to compute
the best response value for each iteration. If none provided, the exact
value is computed.
learning_rate: The learning rate.
"""
self._fp_step += 1
distrib = distribution.DistributionPolicy(self._game, self._policy)
if rl_br_agent:
joint_avg_policy = rl_agent_policy.RLAgentPolicy(
self._game, rl_br_agent, rl_br_agent.player_id, use_observation=True)
br_value = policy_value.PolicyValue(self._game, distrib, joint_avg_policy)
else:
br_value = best_response_value.BestResponse(
self._game, distrib, value.TabularValueFunction(self._game))
greedy_pi = greedy_policy.GreedyPolicy(self._game, None, br_value)
greedy_pi = greedy_pi.to_tabular(states=self._states)
distrib_greedy = distribution.DistributionPolicy(self._game, greedy_pi)
weight = learning_rate if learning_rate else 1.0 / (self._fp_step + 1)
self._policy = MergedPolicy(
self._game, list(range(self._game.num_players())),
[self._policy, greedy_pi], [distrib, distrib_greedy],
[1.0 - weight, weight]).to_tabular(states=self._states)
def nash_conv(self):
"""Returns the nash conv."""
distrib = distribution.DistributionPolicy(self._game, self._policy)
pi_value = policy_value.PolicyValue(self._game, distrib, self._policy)
br_value = best_response_value.BestResponse(self._game, distrib)
return (br_value.eval_state(self._game.new_initial_state()) -
pi_value.eval_state(self._game.new_initial_state()))
def __init__(self, game, policy: policy_std.Policy, root_state=None):
"""Initializes the nash conv.
Args:
game: The game to analyze.
policy: A `policy.Policy` object.
root_state: The state of the game at which to start. If `None`, the game
root state is used.
"""
self._game = game
self._policy = policy
if root_state is None:
self._root_states = game.new_initial_states()
else:
self._root_states = [root_state]
self._distrib = distribution.DistributionPolicy(self._game,
self._policy,
root_state=root_state)
self._pi_value = policy_value.PolicyValue(self._game,
self._distrib,
self._policy,
value.TabularValueFunction(
self._game),
root_state=root_state)
self._br_value = best_response_value.BestResponse(
self._game,
self._distrib,
value.TabularValueFunction(self._game),
root_state=root_state)
def test_cpp_game(self):
"""Checks if the value of a policy computation works."""
game = pyspiel.load_game("mfg_crowd_modelling")
uniform_policy = policy.UniformRandomPolicy(game)
dist = distribution.DistributionPolicy(game, uniform_policy)
py_value = policy_value.PolicyValue(game, dist, uniform_policy)
py_val = py_value(game.new_initial_state())
self.assertAlmostEqual(py_val, 29.92843602293449)
def test_python_game(self): """Checks if the value of a policy computation works.""" game = crowd_modelling.MFGCrowdModellingGame() uniform_policy = policy.UniformRandomPolicy(game) dist = distribution.DistributionPolicy(game, uniform_policy) py_value = policy_value.PolicyValue(game, dist, uniform_policy) py_val = py_value(game.new_initial_state()) self.assertAlmostEqual(py_val, 27.215850929940448)
def test_average(self):
"""Test the average of policies.
Here we test that the average of values is the value of the average policy.
"""
game = crowd_modelling.MFGCrowdModellingGame()
uniform_policy = policy.UniformRandomPolicy(game)
mfg_dist = distribution.DistributionPolicy(game, uniform_policy)
br_value = best_response_value.BestResponse(game, mfg_dist)
py_value = policy_value.PolicyValue(game, mfg_dist, uniform_policy)
greedy_pi = greedy_policy.GreedyPolicy(game, None, br_value)
greedy_pi = greedy_pi.to_tabular()
merged_pi = fictitious_play.MergedPolicy(
game, list(range(game.num_players())), [uniform_policy, greedy_pi],
[mfg_dist,
distribution.DistributionPolicy(game, greedy_pi)], [0.5, 0.5])
merged_pi_value = policy_value.PolicyValue(game, mfg_dist, merged_pi)
self.assertAlmostEqual(merged_pi_value(game.new_initial_state()),
(br_value(game.new_initial_state()) +
py_value(game.new_initial_state())) / 2)
def test_policy_value(self, name):
"""Checks if the value of a policy computation works.
Args:
name: Name of the game.
"""
game = pyspiel.load_game(name)
uniform_policy = policy.UniformRandomPolicy(game)
dist = distribution.DistributionPolicy(game, uniform_policy)
py_value = policy_value.PolicyValue(game, dist, uniform_policy,
value.TabularValueFunction(game))
py_val = py_value(game.new_initial_state())
self.assertAlmostEqual(py_val, 27.215850929940448)
def mean_field_uniform_policy(mfg_game,
number_of_iterations,
compute_metrics=False):
del number_of_iterations
uniform_policy = policy_module.UniformRandomPolicy(mfg_game)
if compute_metrics:
distribution_mfg = distribution_module.DistributionPolicy(
mfg_game, uniform_policy)
policy_value_ = policy_value.PolicyValue(
mfg_game, distribution_mfg,
uniform_policy).value(mfg_game.new_initial_state())
return uniform_policy, policy_value_
return uniform_policy
def nash_conv(self):
"""Returns the nash conv.
Returns:
A list of size `game.num_players()` representing the nash conv for each
population.
"""
distrib = distribution.DistributionPolicy(self._game, self._policy)
pi_value = policy_value.PolicyValue(self._game, distrib, self._policy)
br_value = best_response_value.BestResponse(self._game, distrib)
return [
br_value.eval_state(state) - pi_value.eval_state(state)
for state in self._game.new_initial_states()
]
def test_greedy_cpp(self):
"""Check if the greedy policy works as expected.
The test checks that a greedy policy with respect to an optimal value is
an optimal policy.
"""
game = pyspiel.load_game("mfg_crowd_modelling")
uniform_policy = policy.UniformRandomPolicy(game)
dist = distribution.DistributionPolicy(game, uniform_policy)
br_value = best_response_value.BestResponse(game, dist)
br_val = br_value(game.new_initial_state())
greedy_pi = greedy_policy.GreedyPolicy(game, None, br_value)
greedy_pi = greedy_pi.to_tabular()
pybr_value = policy_value.PolicyValue(game, dist, greedy_pi)
pybr_val = pybr_value(game.new_initial_state())
self.assertAlmostEqual(br_val, pybr_val)
def mean_field_fictitious_play(mfg_game,
number_of_iterations,
compute_metrics=False):
fp = mean_field_fictitious_play_module.FictitiousPlay(mfg_game)
tick_time = time.time()
for _ in range(number_of_iterations):
fp.iteration()
timing = time.time() - tick_time
fp_policy = fp.get_policy()
# print('learning done')
if compute_metrics:
distribution_mfg = distribution_module.DistributionPolicy(
mfg_game, fp_policy)
# print('distribution done')
policy_value_ = policy_value.PolicyValue(
mfg_game, distribution_mfg,
fp_policy).value(mfg_game.new_initial_state())
nash_conv_fp = nash_conv_module.NashConv(mfg_game, fp_policy)
return timing, fp_policy, nash_conv_fp, policy_value_
return timing, fp_policy
def online_mirror_descent_sioux_falls(mfg_game,
number_of_iterations,
md_p=None):
nash_conv_dict = {}
md = md_p if md_p else mirror_descent.MirrorDescent(mfg_game)
tick_time = time.time()
for i in range(number_of_iterations):
md.iteration()
md_policy = md.get_policy()
nash_conv_md = nash_conv_module.NashConv(mfg_game, md_policy)
nash_conv_dict[i] = nash_conv_md.nash_conv()
print((f"Iteration {i}, Nash conv: {nash_conv_md.nash_conv()}, "
"time: {time.time() - tick_time}"))
timing = time.time() - tick_time
md_policy = md.get_policy()
distribution_mfg = distribution_module.DistributionPolicy(
mfg_game, md_policy)
policy_value_ = policy_value.PolicyValue(mfg_game, distribution_mfg,
md_policy).value(
mfg_game.new_initial_state())
nash_conv_md = nash_conv_module.NashConv(mfg_game, md_policy)
return timing, md_policy, nash_conv_md, policy_value_, md, nash_conv_dict
def test_greedy(self, name):
"""Check if the greedy policy works as expected.
The test checks that a greedy policy with respect to an optimal value is
an optimal policy.
Args:
name: Name of the game.
"""
game = pyspiel.load_game(name)
uniform_policy = policy.UniformRandomPolicy(game)
dist = distribution.DistributionPolicy(game, uniform_policy)
br_value = best_response_value.BestResponse(
game, dist, value.TabularValueFunction(game))
br_val = br_value(game.new_initial_state())
greedy_pi = greedy_policy.GreedyPolicy(game, None, br_value)
greedy_pi = greedy_pi.to_tabular()
pybr_value = policy_value.PolicyValue(game, dist, greedy_pi,
value.TabularValueFunction(game))
pybr_val = pybr_value(game.new_initial_state())
self.assertAlmostEqual(br_val, pybr_val)
def online_mirror_descent(mfg_game,
number_of_iterations,
compute_metrics=False,
return_policy=False,
md_p=None):
md = md_p if md_p else mirror_descent.MirrorDescent(mfg_game)
tick_time = time.time()
for _ in range(number_of_iterations):
md.iteration()
timing = time.time() - tick_time
md_policy = md.get_policy()
if compute_metrics:
distribution_mfg = distribution_module.DistributionPolicy(
mfg_game, md_policy)
# print('distribution done')
policy_value_ = policy_value.PolicyValue(
mfg_game, distribution_mfg,
md_policy).value(mfg_game.new_initial_state())
nash_conv_md = nash_conv_module.NashConv(mfg_game, md_policy)
if return_policy:
return timing, md_policy, nash_conv_md, policy_value_, md
return timing, md_policy, nash_conv_md, policy_value_
return timing, md_policy
def main(unused_argv):
logging.info("Loading %s", FLAGS.game_name)
game = pyspiel.load_game(FLAGS.game_name,
GAME_SETTINGS.get(FLAGS.game_name, {}))
uniform_policy = policy.UniformRandomPolicy(game)
mfg_dist = distribution.DistributionPolicy(game, uniform_policy)
envs = [
rl_environment.Environment(game,
mfg_distribution=mfg_dist,
mfg_population=p)
for p in range(game.num_players())
]
info_state_size = envs[0].observation_spec()["info_state"][0]
num_actions = envs[0].action_spec()["num_actions"]
hidden_layers_sizes = [int(l) for l in FLAGS.hidden_layers_sizes]
kwargs = {
"replay_buffer_capacity": FLAGS.replay_buffer_capacity,
"min_buffer_size_to_learn": FLAGS.min_buffer_size_to_learn,
"batch_size": FLAGS.batch_size,
"learn_every": FLAGS.learn_every,
"learning_rate": FLAGS.rl_learning_rate,
"optimizer_str": FLAGS.optimizer_str,
"loss_str": FLAGS.loss_str,
"update_target_network_every": FLAGS.update_target_network_every,
"discount_factor": FLAGS.discount_factor,
"epsilon_decay_duration": FLAGS.epsilon_decay_duration,
"epsilon_start": FLAGS.epsilon_start,
"epsilon_end": FLAGS.epsilon_end,
}
# pylint: disable=g-complex-comprehension
agents = [
dqn.DQN(idx, info_state_size, num_actions, hidden_layers_sizes,
**kwargs) for idx in range(game.num_players())
]
joint_avg_policy = rl_agent_policy.JointRLAgentPolicy(
game, {idx: agent
for idx, agent in enumerate(agents)}, envs[0].use_observation)
if FLAGS.use_checkpoints:
for agent in agents:
if agent.has_checkpoint(FLAGS.checkpoint_dir):
agent.restore(FLAGS.checkpoint_dir)
# Metrics writer will also log the metrics to stderr.
just_logging = FLAGS.logdir is None or jax.host_id() > 0
writer = metric_writers.create_default_writer(FLAGS.logdir,
just_logging=just_logging)
# Save the parameters.
writer.write_hparams(kwargs)
for ep in range(1, FLAGS.num_train_episodes + 1):
if ep % FLAGS.eval_every == 0:
writer.write_scalars(
ep, {
f"agent{i}/loss": float(agent.loss)
for i, agent in enumerate(agents)
})
initial_states = game.new_initial_states()
# Exact best response to uniform.
nash_conv_obj = nash_conv.NashConv(game, uniform_policy)
writer.write_scalars(
ep, {
f"exact_br/{state}": value
for state, value in zip(initial_states,
nash_conv_obj.br_values())
})
# DQN best response to uniform.
pi_value = policy_value.PolicyValue(game, mfg_dist,
joint_avg_policy)
writer.write_scalars(
ep, {
f"dqn_br/{state}": pi_value.eval_state(state)
for state in initial_states
})
if FLAGS.use_checkpoints:
for agent in agents:
agent.save(FLAGS.checkpoint_dir)
for p in range(game.num_players()):
time_step = envs[p].reset()
while not time_step.last():
agent_output = agents[p].step(time_step)
action_list = [agent_output.action]
time_step = envs[p].step(action_list)
# Episode is over, step all agents with final info state.
agents[p].step(time_step)
# Make sure all values were written.
writer.flush()
def main(argv: Sequence[str]) -> None:
# TODO(perolat): move to an example directory.
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
game_settings = {
'only_distribution_reward': True,
'forbidden_states': '[0|0;0|1]',
'initial_distribution': '[0|2;0|3]',
'initial_distribution_value': '[0.5;0.5]',
}
mfg_game = pyspiel.load_game(FLAGS.game, game_settings)
mfg_state = mfg_game.new_initial_state()
while not mfg_state.is_terminal():
print(mfg_state.observation_string(0))
if mfg_state.current_player() == pyspiel.PlayerId.CHANCE:
action_list, prob_list = zip(*mfg_state.chance_outcomes())
action = np.random.choice(action_list, p=prob_list)
mfg_state.apply_action(action)
elif mfg_state.current_player() == pyspiel.PlayerId.MEAN_FIELD:
dist_to_register = mfg_state.distribution_support()
n_states = len(dist_to_register)
dist = [1.0 / n_states for _ in range(n_states)]
mfg_state.update_distribution(dist)
else:
legal_list = mfg_state.legal_actions()
action = np.random.choice(legal_list)
mfg_state.apply_action(action)
print('compute nashconv')
uniform_policy = policy.UniformRandomPolicy(mfg_game)
nash_conv_fp = nash_conv.NashConv(mfg_game, uniform_policy)
print(nash_conv_fp.nash_conv())
print('compute distribution')
mfg_dist = distribution.DistributionPolicy(mfg_game, uniform_policy)
br_value = best_response_value.BestResponse(mfg_game, mfg_dist)
py_value = policy_value.PolicyValue(mfg_game, mfg_dist, uniform_policy)
print(br_value(mfg_game.new_initial_state()))
print(py_value(mfg_game.new_initial_state()))
greedy_pi = greedy_policy.GreedyPolicy(mfg_game, None, br_value)
greedy_pi = greedy_pi.to_tabular()
pybr_value = policy_value.PolicyValue(mfg_game, mfg_dist, greedy_pi)
print(pybr_value(mfg_game.new_initial_state()))
print('merge')
merged_pi = fictitious_play.MergedPolicy(
mfg_game, list(range(mfg_game.num_players())),
[uniform_policy, greedy_pi],
[mfg_dist,
distribution.DistributionPolicy(mfg_game, greedy_pi)], [0.5, 0.5])
merged_pi_value = policy_value.PolicyValue(mfg_game, mfg_dist, merged_pi)
print(br_value(mfg_game.new_initial_state()))
print(py_value(mfg_game.new_initial_state()))
print(merged_pi_value(mfg_game.new_initial_state()))
print((br_value(mfg_game.new_initial_state()) +
py_value(mfg_game.new_initial_state())) / 2)
print('fp')
fp = fictitious_play.FictitiousPlay(mfg_game)
for j in range(100):
print(j)
fp.iteration()
fp_policy = fp.get_policy()
nash_conv_fp = nash_conv.NashConv(mfg_game, fp_policy)
print(nash_conv_fp.nash_conv())
def main(unused_argv):
logging.info("Loading %s", FLAGS.game_name)
game = pyspiel.load_game(FLAGS.game_name,
GAME_SETTINGS.get(FLAGS.game_name, {}))
uniform_policy = policy.UniformRandomPolicy(game)
mfg_dist = distribution.DistributionPolicy(game, uniform_policy)
envs = [
rl_environment.Environment(game,
distribution=mfg_dist,
mfg_population=p)
for p in range(game.num_players())
]
info_state_size = envs[0].observation_spec()["info_state"][0]
num_actions = envs[0].action_spec()["num_actions"]
hidden_layers_sizes = [int(l) for l in FLAGS.hidden_layers_sizes]
kwargs = {
"replay_buffer_capacity": FLAGS.replay_buffer_capacity,
"min_buffer_size_to_learn": FLAGS.min_buffer_size_to_learn,
"batch_size": FLAGS.batch_size,
"learn_every": FLAGS.learn_every,
"learning_rate": FLAGS.rl_learning_rate,
"optimizer_str": FLAGS.optimizer_str,
"loss_str": FLAGS.loss_str,
"update_target_network_every": FLAGS.update_target_network_every,
"discount_factor": FLAGS.discount_factor,
"epsilon_decay_duration": FLAGS.epsilon_decay_duration,
"epsilon_start": FLAGS.epsilon_start,
"epsilon_end": FLAGS.epsilon_end,
}
# pylint: disable=g-complex-comprehension
agents = [
dqn.DQN(idx, info_state_size, num_actions, hidden_layers_sizes,
**kwargs) for idx in range(game.num_players())
]
joint_avg_policy = DQNPolicies(envs, agents)
if FLAGS.use_checkpoints:
for agent in agents:
if agent.has_checkpoint(FLAGS.checkpoint_dir):
agent.restore(FLAGS.checkpoint_dir)
for ep in range(FLAGS.num_train_episodes):
if (ep + 1) % FLAGS.eval_every == 0:
losses = [agent.loss for agent in agents]
logging.info("Losses: %s", losses)
nash_conv_obj = nash_conv.NashConv(game, uniform_policy)
print(
str(ep + 1) + " Exact Best Response to Uniform " +
str(nash_conv_obj.br_values()))
pi_value = policy_value.PolicyValue(game, mfg_dist,
joint_avg_policy)
print(
str(ep + 1) + " DQN Best Response to Uniform " + str([
pi_value.eval_state(state)
for state in game.new_initial_states()
]))
if FLAGS.use_checkpoints:
for agent in agents:
agent.save(FLAGS.checkpoint_dir)
logging.info("_____________________________________________")
for p in range(game.num_players()):
time_step = envs[p].reset()
while not time_step.last():
agent_output = agents[p].step(time_step)
action_list = [agent_output.action]
time_step = envs[p].step(action_list)
# Episode is over, step all agents with final info state.
agents[p].step(time_step)
def main(argv: Sequence[str]) -> None:
# TODO(perolat): move to an example directory.
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
mfg_game = pyspiel.load_game(FLAGS.game, GAME_SETTINGS.get(FLAGS.game, {}))
mfg_state = mfg_game.new_initial_state()
print('Playing a single arbitrary trajectory')
while not mfg_state.is_terminal():
print('State obs string:', mfg_state.observation_string(0))
if mfg_state.current_player() == pyspiel.PlayerId.CHANCE:
action_list, prob_list = zip(*mfg_state.chance_outcomes())
action = np.random.choice(action_list, p=prob_list)
mfg_state.apply_action(action)
elif mfg_state.current_player() == pyspiel.PlayerId.MEAN_FIELD:
dist_to_register = mfg_state.distribution_support()
n_states = len(dist_to_register)
dist = [1.0 / n_states for _ in range(n_states)]
mfg_state.update_distribution(dist)
else:
legal_list = mfg_state.legal_actions()
action = np.random.choice(legal_list)
mfg_state.apply_action(action)
print('compute nashconv')
uniform_policy = policy.UniformRandomPolicy(mfg_game)
nash_conv_fp = nash_conv.NashConv(mfg_game, uniform_policy)
print('Nashconv:', nash_conv_fp.nash_conv())
print('compute distribution')
mfg_dist = distribution.DistributionPolicy(mfg_game, uniform_policy)
br_value = best_response_value.BestResponse(
mfg_game, mfg_dist, value.TabularValueFunction(mfg_game))
py_value = policy_value.PolicyValue(mfg_game, mfg_dist, uniform_policy,
value.TabularValueFunction(mfg_game))
print(
'Value of a best response policy to a uniform policy '
'(computed with best_response_value)',
br_value(mfg_game.new_initial_state()))
print('Value of the uniform policy:', py_value(mfg_game.new_initial_state()))
greedy_pi = greedy_policy.GreedyPolicy(mfg_game, None, br_value)
greedy_pi = greedy_pi.to_tabular()
pybr_value = policy_value.PolicyValue(mfg_game, mfg_dist, greedy_pi,
value.TabularValueFunction(mfg_game))
print(
'Value of a best response policy to a uniform policy (computed at the '
'value of the greedy policy of the best response value)',
pybr_value(mfg_game.new_initial_state()))
print('merge')
merged_pi = fictitious_play.MergedPolicy(
mfg_game, list(range(mfg_game.num_players())),
[uniform_policy, greedy_pi],
[mfg_dist, distribution.DistributionPolicy(mfg_game, greedy_pi)],
[0.5, 0.5])
merged_pi_value = policy_value.PolicyValue(
mfg_game, mfg_dist, merged_pi, value.TabularValueFunction(mfg_game))
print(br_value(mfg_game.new_initial_state()))
print(py_value(mfg_game.new_initial_state()))
print(merged_pi_value(mfg_game.new_initial_state()))
print((br_value(mfg_game.new_initial_state()) +
py_value(mfg_game.new_initial_state())) / 2)
print('fp')
fp = fictitious_play.FictitiousPlay(mfg_game)
for j in range(100):
print('Iteration', j, 'of fictitious play')
fp.iteration()
fp_policy = fp.get_policy()
nash_conv_fp = nash_conv.NashConv(mfg_game, fp_policy)
print('Nashconv of the current FP policy', nash_conv_fp.nash_conv())
print('md')
md = mirror_descent.MirrorDescent(mfg_game,
value.TabularValueFunction(mfg_game))
for j in range(10):
print('Iteration', j, 'of mirror descent')
md.iteration()
md_policy = md.get_policy()
nash_conv_md = nash_conv.NashConv(mfg_game, md_policy)
print('Nashconv of the current MD policy', nash_conv_md.nash_conv())
def test_softmax(self, name):
"""Check if the softmax policy works as expected.
The test checks that:
- uniform prior policy gives the same results than no prior.
- very high temperature gives almost a uniform policy.
- very low temperature gives almost a deterministic policy for the best
action.
Args:
name: Name of the game.
"""
game = pyspiel.load_game(name)
uniform_policy = policy.UniformRandomPolicy(game)
dist = distribution.DistributionPolicy(game, uniform_policy)
br_value = best_response_value.BestResponse(
game, dist, value.TabularValueFunction(game))
br_init_val = br_value(game.new_initial_state())
# uniform prior policy gives the same results than no prior.
softmax_pi_uniform_prior = softmax_policy.SoftmaxPolicy(
game, None, 1.0, br_value, uniform_policy).to_tabular()
softmax_pi_uniform_prior_value = policy_value.PolicyValue(
game, dist, softmax_pi_uniform_prior,
value.TabularValueFunction(game))
softmax_pi_uniform_prior_init_val = softmax_pi_uniform_prior_value(
game.new_initial_state())
softmax_pi_no_prior = softmax_policy.SoftmaxPolicy(
game, None, 1.0, br_value, None)
softmax_pi_no_prior_value = policy_value.PolicyValue(
game, dist, softmax_pi_no_prior, value.TabularValueFunction(game))
softmax_pi_no_prior_init_val = softmax_pi_no_prior_value(
game.new_initial_state())
self.assertAlmostEqual(softmax_pi_uniform_prior_init_val,
softmax_pi_no_prior_init_val)
# very high temperature gives almost a uniform policy.
uniform_policy = uniform_policy.to_tabular()
uniform_value = policy_value.PolicyValue(
game, dist, uniform_policy, value.TabularValueFunction(game))
uniform_init_val = uniform_value(game.new_initial_state())
softmax_pi_no_prior = softmax_policy.SoftmaxPolicy(
game, None, 100000000, br_value, None)
softmax_pi_no_prior_value = policy_value.PolicyValue(
game, dist, softmax_pi_no_prior, value.TabularValueFunction(game))
softmax_pi_no_prior_init_val = softmax_pi_no_prior_value(
game.new_initial_state())
self.assertAlmostEqual(uniform_init_val, softmax_pi_no_prior_init_val)
# very low temperature gives almost a best response policy.
softmax_pi_no_prior = softmax_policy.SoftmaxPolicy(
game, None, 0.0001, br_value, None)
softmax_pi_no_prior_value = policy_value.PolicyValue(
game, dist, softmax_pi_no_prior, value.TabularValueFunction(game))
softmax_pi_no_prior_init_val = softmax_pi_no_prior_value(
game.new_initial_state())
self.assertAlmostEqual(br_init_val, softmax_pi_no_prior_init_val)
