def test_lemke_howson_enumeration(self):
"""Test for the enumeration of equilibrium using Lemke Howson"""
A = np.array([[3, 1], [0, 2]])
B = np.array([[2, 1], [0, 3]])
g = nash.Game(A, B)
expected_equilibria = [(np.array([1, 0]), np.array([1, 0])),
(np.array([0, 1]), np.array([0, 1]))] * 2
equilibria = g.lemke_howson_enumeration()
for equilibrium, expected_equilibrium in zip(equilibria,
expected_equilibria):
for strategy, expected_strategy in zip(equilibrium,
expected_equilibrium):
self.assertTrue(all(np.isclose(strategy, expected_strategy)))
A = np.array([[3, 1], [1, 3]])
B = np.array([[1, 3], [3, 1]])
g = nash.Game(A, B)
expected_equilibria = [(np.array([1 / 2, 1 / 2
]), np.array([1 / 2, 1 / 2]))] * 4
equilibria = g.lemke_howson_enumeration()
for equilibrium, expected_equilibrium in zip(equilibria,
expected_equilibria):
for strategy, expected_strategy in zip(equilibrium,
expected_equilibrium):
self.assertTrue(all(np.isclose(strategy, expected_strategy)))
def NashEquilibriumSolver(A, B=None):
"""
Quickly solve *one* Nash equilibrium with Lemke Howson algorithm, given *degenerate* (det not equal to 0) payoff matrix.
Ref: https://nashpy.readthedocs.io/en/stable/reference/lemke-howson.html#lemke-howson
TODO: sometimes give nan or wrong dimensions, check here: https://github.com/drvinceknight/Nashpy/issues/35
"""
# print('Determinant of matrix: ', np.linalg.det(A))
if B is not None:
rps = nash.Game(A, B)
else:
rps = nash.Game(A) # zero-sum game: unimatrix
dim = A.shape[0]
final_eq = None
# To handle the problem that sometimes Lemke-Howson implementation will give
# wrong returned NE shapes or NAN in value, use different initial_dropped_label value
# to find a valid one.
for l in range(0, sum(A.shape) - 1):
# Lemke Howson can not solve degenerate matrix.
# eq = rps.lemke_howson(initial_dropped_label=l) # The initial_dropped_label is an integer between 0 and sum(A.shape) - 1
# Lexicographic Lemke Howson can solve degenerate matrix: https://github.com/newaijj/Nashpy/blob/ffea3522706ad51f712d42023d41683c8fa740e6/tests/unit/test_lemke_howson_lex.py#L9
eq = lemke_howson_lex(A, -A, initial_dropped_label=l)
if eq[0].shape[0] == dim and eq[1].shape[0] == dim and not np.isnan(
eq[0]).any() and not np.isnan(eq[1]).any():
# valid shape and valid value (not nan)
final_eq = eq
break
if final_eq is None:
raise ValueError('No valid Nash equilibrium is found!')
return final_eq
def adaptive_strategy(p_list_1, p_list_2, n_split=40, n_rep=40):
mean_diff_1 = split_and_compare_perturbed_games(p_list_1, n_split=n_split, n_rep=n_rep)
mean_diff_2 = split_and_compare_perturbed_games(p_list_2, n_split=n_split, n_rep=n_rep, player_ix=1)
averaging_is_better_1 = mean_diff_1 > 0
averaging_is_better_2 = mean_diff_2 > 0
_, _, p1_1, p2_1 = get_payoffs_from_list(p_list_1)
_, _, p1_2, p2_2 = get_payoffs_from_list(p_list_2)
p1_avg = (p1_1 + p2_1) / 2
p2_avg = (p2_1 + p2_2) / 2
# if averaging_is_better_1:
# a1 = get_welfare_optimal_eq(nash.Game(p1_avg, p2_avg))[0]
# else:
# a1 = get_welfare_optimal_eq(nash.Game(p1_1, p2_1))[0]
# if averaging_is_better_2:
# a2 = get_welfare_optimal_eq(nash.Game(p1_avg, p2_avg))[1]
# else:
# a2 = get_welfare_optimal_eq(nash.Game(p1_2, p2_2))[1]
if averaging_is_better_1 and averaging_is_better_2:
a1, a2, _ = get_welfare_optimal_eq(nash.Game(p1_avg, p2_avg))
else:
a1 = get_welfare_optimal_eq(nash.Game(p1_1, p2_1))[0]
a2 = get_welfare_optimal_eq(nash.Game(p1_2, p2_2))[1]
return a1, a2, mean_diff_1, mean_diff_2
def NashEqu(output, Game, state, j):
M = Game.createGameMatrix(state)
flag , V = saddlePoint(M)
if flag:
output.put((j, V))
else:
# if np.linalg.det(M) != 0:
# iden = np.array([1, 1, 1, 1])
# V = 1 / (iden.dot(np.linalg.inv(M)).dot(iden.T))
# output.put((j, V))
# print("Not singular Matrix, use Game theory method, V is:", V)
# if V>100 or V<-100:
# print("M is:", M)
# print("wdnmd")
# else:
rps = nash.Game(M)
# print("state is: ", state, "M is: ", M)
eqs = rps.support_enumeration()
flag_su = 0
for eq in eqs:
# policy_ct = np.round(eq[0], 6)
# policy_ad = np.round(eq[1], 6)
policy_ct = eq[0]
policy_ad = eq[1]
reward = rps[policy_ct, policy_ad]
reward_ct = reward[0]
if math.isnan(reward_ct) == False and math.isinf(reward_ct) == False and abs(reward_ct) <=100 and sum(abs(policy_ct))<1.1 and sum(abs(policy_ad))<1.1:
flag_su = 1
break
if flag_su == 1:
# print("Use support_enumeration, V is:", reward_ct)
output.put((j, reward_ct))
else:
# except UnboundLocalError: ##Here, we can not use support_enumeration, try vertex enumeration
rps = nash.Game(M)
eqs = rps.vertex_enumeration()
flag_ver = 0
for eq in eqs:
# policy_ct = np.round(eq[0], 6)
# policy_ad = np.round(eq[1], 6)
policy_ct = eq[0]
policy_ad = eq[1]
reward = rps[policy_ct, policy_ad]
reward_ct = reward[0]
if math.isnan(reward_ct) == False and math.isinf(reward_ct) == False and abs(reward_ct) <=100 and sum(abs(policy_ct))<1.1 and sum(abs(policy_ad))<1.1:
flag_ver = 1
break
if flag_ver == 1:
# print("Use vertex_enumeration, V is:", reward_ct)
output.put((j, reward_ct))
else:
print ("WDNMD")
print("M is:", M)
print("state is:", state)
def NashEquilibriaSolver(A, B=None):
"""
Given payoff matrix/matrices, return a list of existing Nash equilibria:
[(nash1_p1, nash1_p2), (nash2_p1, nash2_p2), ...]
"""
if B is not None:
rps = nash.Game(A, B)
else:
rps = nash.Game(A) # zero-sum game: unimatrix
# eqs = rps.support_enumeration()
eqs = rps.vertex_enumeration()
return list(eqs)
def test_vertex_enumeration_for_bi_matrix(self):
"""Test for the equilibria calculation using vertex enumeration"""
A = np.array([[160, 205, 44], [175, 180, 45], [201, 204, 50],
[120, 207, 49]])
B = np.array([[2, 2, 2], [1, 0, 0], [3, 4, 1], [4, 1, 2]])
g = nash.Game(A, B)
expected_equilibria = [(np.array([0, 0, 3 / 4, 1 / 4]),
np.array([1 / 28, 27 / 28, 0]))]
for obtained, expected in zip(g.vertex_enumeration(),
expected_equilibria):
for s1, s2 in zip(obtained, expected):
self.assertTrue(
all(np.isclose(s1, s2)),
msg="obtained: {} !=expected: {}".format(
obtained, expected),
)
A = np.array([[1, 0], [-2, 3]])
B = np.array([[3, 2], [-1, 0]])
g = nash.Game(A, B)
expected_equilibria = [
(np.array([1, 0]), np.array([1, 0])),
(np.array([0, 1]), np.array([0, 1])),
(np.array([1 / 2, 1 / 2]), np.array([1 / 2, 1 / 2])),
]
for obtained, expected in zip(g.vertex_enumeration(),
expected_equilibria):
for s1, s2 in zip(obtained, expected):
self.assertTrue(
all(np.isclose(s1, s2)),
msg="obtained: {} !=expected: {}".format(
obtained, expected),
)
A = np.array([[2, 1], [0, 2]])
B = np.array([[2, 0], [1, 2]])
g = nash.Game(A, B)
expected_equilibria = [
(np.array([1, 0]), np.array([1, 0])),
(np.array([0, 1]), np.array([0, 1])),
(np.array([1 / 3, 2 / 3]), np.array([1 / 3, 2 / 3])),
]
for obtained, expected in zip(g.vertex_enumeration(),
expected_equilibria):
for s1, s2 in zip(obtained, expected):
self.assertTrue(
all(np.isclose(s1, s2)),
msg="obtained: {} !=expected: {}".format(
obtained, expected),
)
def test_zero_sum_game_init(self, A):
"""Test that can create a zero sum game"""
g = nash.Game(A)
self.assertTrue(np.array_equal(g.payoff_matrices[0], A))
self.assertTrue(
np.array_equal(g.payoff_matrices[0], -g.payoff_matrices[1]))
self.assertTrue(g.zero_sum)
# Can also init with lists
A = A.tolist()
g = nash.Game(A)
self.assertTrue(np.array_equal(g.payoff_matrices[0], np.asarray(A)))
self.assertTrue(
np.array_equal(g.payoff_matrices[0], -g.payoff_matrices[1]))
self.assertTrue(g.zero_sum)
def test_bi_matrix_init(self, A, B):
"""Test that can create a bi matrix game"""
g = nash.Game(A, B)
self.assertEqual(g.payoff_matrices, (A, B))
if np.array_equal(A, -B): # Check if A or B are non zero
self.assertTrue(g.zero_sum)
else:
self.assertFalse(g.zero_sum)
# Can also init with lists
A = A.tolist()
B = B.tolist()
g = nash.Game(A, B)
self.assertTrue(np.array_equal(g.payoff_matrices[0], np.asarray(A)))
self.assertTrue(np.array_equal(g.payoff_matrices[1], np.asarray(B)))
def test_stochastic_fictitious_play(self, A, B, seed):
"""Test for the stochastic fictitious play algorithm"""
np.random.seed(seed)
iterations = 10
g = nash.Game(A, B)
expected_outcome = tuple(
nashpy.learning.stochastic_fictitious_play.
stochastic_fictitious_play(*g.payoff_matrices,
iterations=iterations))
np.random.seed(seed)
outcome = tuple(g.stochastic_fictitious_play(iterations=iterations))
assert len(outcome) == iterations + 1
assert len(expected_outcome) == iterations + 1
for (plays, distributions), (
expected_plays,
expected_distributions,
) in zip(outcome, expected_outcome):
row_play, column_play = plays
expected_row_play, expected_column_play = expected_plays
row_dist, column_dist = distributions
expected_row_dist, expected_column_dist = expected_distributions
assert np.allclose(column_dist, expected_column_dist)
assert np.allclose(row_dist, expected_row_dist)
assert np.allclose(column_play, expected_column_play)
assert np.allclose(row_play, expected_row_play)
def Nash(state, Agent1, Agent2):
'''Calculate nash equilibrium on current state and Q(s) as rewards.'''
Agent1.check_state_exist(state)
Agent2.check_state_exist(state)
q_1, q_2 = [], []
for action1 in Agent1.actions:
row_q_1, row_q_2 = [], []
for action2 in Agent2.actions:
joint_action = (action1, action2)
row_q_1.append(Agent1.q_table.loc[state][joint_action])
row_q_2.append(Agent2.q_table.loc[state][joint_action])
q_1.append(row_q_1)
q_2.append(row_q_2)
game = nashpy.Game(np.array(q_1), np.array(q_2))
equilibria = game.lemke_howson_enumeration()
eq_list = list(equilibria)
pi = None
for eq in eq_list:
if eq[0].shape == (len(Agent1.actions), ) and eq[1].shape == (len(
Agent2.actions), ):
if any(np.isnan(eq[0])) is False and any(np.isnan(eq[1])) is False:
pi = eq
break
if pi is None:
pi1 = np.repeat(1.0 / len(Agent1.actions), len(Agent1.actions))
pi2 = np.repeat(1.0 / len(Agent2.actions), len(Agent2.actions))
pi = (pi1, pi2)
return pi[0], pi[1]
def random_nash(sigma_x=1, n=10):
u1_mean = np.array([[-10, 0], [-3, -1]])
u2_mean = np.array([[-10, -3], [0, -1]])
sigma_x_mat = np.array([[sigma_x, sigma_x], [0., 0.]])
u1_1 = np.random.normal(loc=u1_mean, scale=sigma_x_mat, size=(n, 2, 2))
u1_2 = np.random.normal(loc=u2_mean, scale=sigma_x_mat, size=(n, 2, 2))
u2_1 = np.random.normal(loc=u1_mean, scale=sigma_x_mat, size=(n, 2, 2))
u2_2 = np.random.normal(loc=u2_mean, scale=sigma_x_mat, size=(n, 2, 2))
crash_lst = []
for i in range(n):
u1_1_i, u1_2_i, u2_1_i, u2_2_i = u1_1[i], u1_2[i], u2_1[i], u2_2[i]
a1_1, _, _ = get_welfare_optimal_eq(nash.Game(u1_1_i, u1_2_i))
_, a2_2, _ = get_welfare_optimal_eq(nash.Game(u2_1_i, u2_2_i))
crash = (a1_1[0] == a2_2[0] == 1)
crash_lst.append(crash)
print(np.mean(crash_lst))
def nash_strategy(solver, return_all=False):
"""Returns nash distribution on meta game matrix.
This method only works for two player general-sum games.
Args:
solver: GenPSROSolver instance.
return_all: if return all NE or random one.
Returns:
Nash distribution on strategies.
"""
meta_games = solver.get_meta_game
if not isinstance(meta_games, list):
meta_games = [meta_games, -meta_games]
if len(meta_games) > 2:
raise ValueError(
"Nash solver only works for two player general-sum games. Number of players > 2."
)
p1_payoff = meta_games[0]
p2_payoff = meta_games[1]
game = nash.Game(p1_payoff, p2_payoff)
NE_list = []
for eq in game.support_enumeration():
NE_list.append(eq)
if return_all:
return NE_list
else:
return list(np.random.choice(NE_list))
def updateNashEquilibrium(self, number_of_games):
reward_matrix = np.zeros(
(self.number_of_agents, self.number_of_agents))
for i in range(self.number_of_agents):
for j in range(i + 1, self.number_of_agents):
average_reward, _ = self.game_tester.getAverageReward(
self.agents[i], self.agents[j], number_of_games)
reward_matrix[i][j] = average_reward
reward_matrix[j][i] = -average_reward
if True:
for i in range(self.number_of_agents):
print(reward_matrix[i])
x = nash.Game(reward_matrix).support_enumeration()
self.nash_distribution = torch.FloatTensor(list(x)[0][0]) # Don't ask
# Add epsilon to the nash equilibrium and normalise
eps = 0.2
for i in range(self.number_of_agents):
self.nash_distribution[
i] = eps / self.number_of_agents + self.nash_distribution[
i] / (1 + eps)
print(self.nash_distribution)
self.nash_categorical = Categorical(self.nash_distribution)
def test_incorrect_dimensions_init(self):
"""Tests that ValueError is raised for unequal dimensions"""
A = np.array([[1, 2, 3], [4, 5, 6]])
B = np.array([[1, 2], [3, 4]])
with pytest.raises(ValueError):
nash.Game(A, B)
def nash_mixture_from_payoff(self, payoffa, payoffb, sgs, sds):
config = self.config
def _update_g(p):
p = np.reshape(p, [-1])
result = self.destructive_mixture_g(p)
return p, result
def _update_d(p):
p = np.reshape(p, [-1])
result = self.destructive_mixture_d(p)
return p, result
if self.config.nash_method == 'support':
try:
u = next(nash.Game(payoffa, payoffb).support_enumeration())
except (StopIteration):
print("Nashpy 'support' iteration failed. Using 1,0,0...")
u = [
list(np.zeros(len(self.sds))),
list(np.zeros(len(self.sgs)))
]
u[0][0] = 1.
u[1][0] = 1.
elif self.config.nash_method == 'lemke':
u = next(nash.Game(payoffa, payoffb).lemke_howson_enumeration())
else:
try:
u = next(nash.Game(payoffa, payoffb).vertex_enumeration())
except (StopIteration, scipy.spatial.qhull.QhullError):
print("Nashpy 'vertex' iteration failed. Using 1,0,0...")
u = [
list(np.zeros(len(self.sds))),
list(np.zeros(len(self.sgs)))
]
u[0][0] = 1.
u[1][0] = 1.
if len(u[0]) != len(self.sgs):
return [None, None, None, None]
p1, p1result = _update_g(u[0])
p2, p2result = _update_d(u[1])
return p1, p1result, p2, p2result
def test_support_enumeration_for_deg_bi_matrix_game_with_low_tol(self):
A = np.array([[0, 0], [0, 0]])
g = nash.Game(A)
with warnings.catch_warnings(record=True) as w:
obtained_equilibria = list(g.support_enumeration(tol=0))
self.assertEqual(len(obtained_equilibria), 4)
self.assertGreater(len(w), 0)
self.assertEqual(w[-1].category, RuntimeWarning)
def __init__(self, A, B):
Game.__init__(self)
"""
5,1 3,5
2,0 2,0
A = [[5, 3], [2, 2]]
B = [[1, 5], [0, 0]]
"""
self.game = nash.Game(A, B)
def test_replicator_dynamics(self):
"""Test for the replicator dynamics algorithm"""
A = np.array([[3, 2], [4, 1]])
game = nash.Game(A)
y0 = np.array([0.9, 0.1])
timepoints = np.linspace(0, 10, 100)
xs = game.replicator_dynamics(y0, timepoints)
expected_xs = np.array([[0.50449178, 0.49550822]])
assert np.allclose(xs[-1], expected_xs)
def get_equilibria(p, c, low_cost, high_cost, commit_prior_if_committed,
commit_prior_if_not_committed, cost_prior):
payoffs_threatener, payoffs_target = create_payoff_matrix(
p, c, low_cost, high_cost, commit_prior_if_committed,
commit_prior_if_not_committed, cost_prior)
game = nashpy.Game(payoffs_threatener, payoffs_target)
eqs = list(game.support_enumeration())
# ToDo: check for multiple equilibria?
return game, eqs[0]
def solve_nashpy(game):
import nashpy as nash
rps = nash.Game(game)
eqs = list(rps.support_enumeration())
y = eqs[0]
z = eqs[1]
return y, z
def calculate_equilibria_vertex_enum(self, A=None, B=None):
"""
:return: List of equilibria for the game found via support enumeration
"""
if A is None and B is None:
A, B = self.dask_A, self.dask_B
game = nash.Game(A, B)
equilibria = game.vertex_enumeration()
return list(equilibria)
def calc_NE(pa_payoff, po_payoff):
game = nash.Game(pa_payoff, po_payoff)
for i in range(pa_payoff.shape[0]):
try:
eq = game.lemke_howson(i)
except RuntimeWarning:
pass
else:
break
return list(eq)[0], list(eq)[1]
def test_lemke_howson_for_bi_matrix(self):
"""Test for the equilibria calculation using lemke howson"""
A = np.array([[160, 205, 44], [175, 180, 45], [201, 204, 50],
[120, 207, 49]])
B = np.array([[2, 2, 2], [1, 0, 0], [3, 4, 1], [4, 1, 2]])
g = nash.Game(A, B)
expected_equilibria = (np.array([0, 0, 3 / 4, 1 / 4]),
np.array([1 / 28, 27 / 28, 0]))
equilibria = g.lemke_howson(initial_dropped_label=4)
for eq, expected in zip(equilibria, expected_equilibria):
self.assertTrue(all(np.isclose(eq, expected)))
def value_of_matrix1(M):
'using Nashpy'
zsgame = nash.Game(M)
label = 0
eq = zsgame.lemke_howson(initial_dropped_label=label)
#f = np.ones(np.shape(M)[0])/ np.shape(M)[0]
#g = np.empty(np.shape(M)[1])/ np.shape(M)[1]
#v = 0
f, g = eq[0], eq[1]
v = zsgame[f, g][0]
return v
def game_theory():
A = np.array([[0, -1, 1], [1, 0, -1], [-1, 1, 0]])
B = -A
rps = nash.Game(A, B)
print(rps)
#utility of a pair strategies
sigma_c = [1 / 2, 1 / 2, 0]
sigma_r = [0, 1 / 2, 1 / 2]
print(rps[sigma_r, sigma_c])
#Nash equilibria
eqs = rps.support_enumeration()
print(list(eqs))
#prisoners dillema
P1 = np.array([[3, 0], [4, 1]])
P2 = np.array([[3, 4], [0, 1]])
prisoner_dilemma = nash.Game(P1, P2)
print(prisoner_dilemma)
eqs = prisoner_dilemma.support_enumeration()
print(eqs)
return
def nash_equilibrium(self):
p_r = []
p_c = []
game = nash.Game(self.Rowena, self.Collin)
for eq in game.support_enumeration():
p_r.append(eq[0])
p_c.append(eq[1])
print("The nash equilibrium probabilities are:")
for index, _ in enumerate(p_r):
print("(p={}, q={})".format(p_r[index][0], p_c[index][0]))
def find_nash(full_matrix, disp_strategy_idx):
matrix = full_matrix[disp_strategy_idx, :, :, 1:]
A = extract_player_utility(matrix, 0)
B = extract_player_utility(matrix, 1)
game = nash.Game(A, B)
equs = skip_mixed_strategy(game.support_enumeration())
if equs and is_system_consistent(equs, A, B):
sol = extract_solution(equs)
return sol
else:
return 'Inconsistent'
def get_prob_of_defection(payoff_matrix, support_enumeration=True):
"""
A function which computes the Nash Equilibria of the game using one of three algorithms and returns a dictionary of the Nash Equilibria, the maximum and minimum probabilities of defection within the equilibria and whether the game could be degenerate. The input variables are:
'payoff_matrix', a numpy array containing the payoffs obtained for each action of the game; and
'support_enumeration', a boolean variable stating whether the support enumeration algorithm (if evaluated true) or the vertex enumeration algorithm (if evaluated False) is used to calculate the Nash equilibria.
"""
game = nash.Game(payoff_matrix, payoff_matrix.transpose())
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
if support_enumeration == True:
nash_equilibria = list(game.support_enumeration())
else:
highlight_numpy_warning = np.seterr(all="warn")
nash_equilibria = list(game.vertex_enumeration())
if len(w) == 0:
warning_message = None
else:
warning_message = str([w[i].message for i in range(len(w))])
if (len(nash_equilibria) == 0) or ("-Inf" in nash_equilibria):
nash_equilibria = None
prob_of_defection_in_equilibria = None
least_prob_of_defection_in_equilibria = None
greatest_prob_of_defection_in_equilibria = None
else:
prob_of_defection_in_equilibria = [
sigma_1[-1] for sigma_1, _ in nash_equilibria
]
least_prob_of_defection_in_equilibria = min(
prob_of_defection_in_equilibria)
greatest_prob_of_defection_in_equilibria = max(
prob_of_defection_in_equilibria)
get_prob_of_defect_output_dict = {
"nash equilibria": np.array(nash_equilibria),
"least prob of defect": least_prob_of_defection_in_equilibria,
"greatest prob of defect": greatest_prob_of_defection_in_equilibria,
"warning message": str(warning_message),
}
return get_prob_of_defect_output_dict
def test_property_support_enumeration(self, A, B):
"""Property based test for the equilibria calculation"""
g = nash.Game(A, B)
for equilibrium in g.support_enumeration():
for i, s in enumerate(equilibrium):
# Test that have a probability vector (subject to numerical
# error)
self.assertAlmostEqual(s.sum(), 1)
# Test that it is of the correct size
self.assertEqual(s.size, [3, 4][i])
# Test that it is non negative
self.assertTrue(all(s >= 0))
def test_zero_sum_repr(self):
"""Test that can create a bi matrix game"""
A = np.array([[1, -1], [-1, 1]])
g = nash.Game(A)
string_repr = """Zero sum game with payoff matrices:
Row player:
[[ 1 -1]
[-1 1]]
Column player:
[[-1 1]
[ 1 -1]]"""
self.assertEqual(g.__repr__(), string_repr)