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 iteration(self):
"""Returns a new `TabularPolicy` equivalent to this policy."""
self._fp_step += 1
distrib = distribution.DistributionPolicy(self._game, self._policy)
br_value = best_response_value.BestResponse(self._game, distrib)
greedy_pi = greedy_policy.GreedyPolicy(self._game, None, br_value)
greedy_pi = greedy_pi.to_tabular()
distrib_greedy = distribution.DistributionPolicy(self._game, greedy_pi)
self._policy = MergedPolicy(
self._game, list(range(self._game.num_players())),
[self._policy, greedy_pi], [distrib, distrib_greedy],
[1.0*self._fp_step/(self._fp_step+1), 1.0/(self._fp_step+1)]
).to_tabular()
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 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_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 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 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())
