def gen_setting(T, P, targets_dict, time_horizon):
import source.players.attackers as attackers
import source.game as game
targets = translate(targets_dict[T][0])
distributions = []
for i in range(P):
distributions.append(tuple(gen_distr(T)))
values = tuple((v, v) for v in targets)
g = game.Game(values, time_horizon)
g.attackers = [1]
g.defenders = [0]
#print(T, targets)
att = [attackers.StackelbergAttacker(g, 1)]
for d in distributions:
#print(d)
att.append(attackers.StochasticAttacker(g, 1, 1, *d))
profiles = []
index = 0 # hardcoded for stackelberg
profiles.append({
"attacker": print_adv(att[index]),
"others": [print_adv(a) for a in att if a != att[index]],
"i": index
})
return targets, profiles
def main2(arguments):
"""
SG with a Stackelberg attacker and a defender who can distinguish
between a Stackelberg adn a Uniform
"""
values = ((1, 1), (2, 2), (3, 3))
time_horizon = 10
g = game.Game(values, time_horizon)
agent = player.StUDefender(g, 0)
# attacker = player.StackelbergAttacker(g, 1)
attacker = player.Attacker(g, 1)
g.set_players([agent], [attacker])
e = environment.Environment(g, 0)
for t in range(g.time_horizon):
strategy = agent.compute_strategy()
e.observe_strategy(strategy)
realization = agent.sample_strategy()
e.observe_realization(realization)
feedback = e.feedback("expert")
agent.receive_feedback(feedback)
print("history of the game")
for i, h in enumerate(g.history):
print("strategies at " + str(i) + ":")
print("\t agent:", g.strategy_history[i][0], "\t attacker:",
g.strategy_history[i][1])
print("moves at :" + str(i) + ":")
print("\t agent:", h[0], "\t attacker:", h[1])
def translate(targets):
import source.game as game
m = min(targets)
r = round(float(np.random.uniform(high=m)), 3)
targets2 = [x - r for x in targets]
values = tuple((v, v) for v in targets2)
g = game.Game(values, 1)
g.attackers = [1]
g.defenders = [0]
if len(targets) == len(support(g)):
return targets2
else:
return translate(targets)
def main3(arguments):
"""
You can take advantage also of the already implemented functions of
Experiment class
"""
values = ((1, 1), (2, 2), (3, 3))
time_horizon = 10
g = game.Game(values, time_horizon)
agent = player.StUDefender(g, 0)
# attacker = player.StackelbergAttacker(g, 1)
attacker = player.Attacker(g, 1)
g.set_players([agent], [attacker])
experiment = Experiment(g)
experiment.run()
# select an existent folder to save the results
experiment.save_results(".")
def gen_tar_with_len(length):
import source.game as game
len_s = 0
while len_s != length:
T = 15
if length == 10:
T = 50
time_horizon = 10
targets = [round(x, 3) for x in gen_norm_targets(T)]
values = tuple((v, v) for v in targets)
g = game.Game(values, time_horizon)
g.attackers = [1]
g.defenders = [0]
s = support(g)
len_s = len(s)
return s
def setUp(self):
time_horizon = 10
self.targets = [1, 2]
values = tuple((v, v) for v in self.targets)
self.game = game.Game(values, time_horizon)
defender = player.Defender(self.game, 0)
attacker = player.Attacker(self.game, 1)
other = player.Attacker(self.game, 1)
self.game.set_players([defender], [attacker], [attacker, other])
self.game.history = [{
0: [0],
1: [0]
}, {
0: [1],
1: [0]
}, {
0: [0],
1: [1]
}, {
0: [1],
1: [1]
}]
def main(arguments):
"""
SG with a Stackelberg attacker and a defender who play
a uniform strategy
"""
values = ((1, 1), (2, 2), (3, 3))
time_horizon = 10
g = game.Game(values, time_horizon)
agent = player.Defender(g, 0, 1)
attacker = player.StackelbergAttacker(g, 1, 1)
g.set_players([agent], [attacker])
e = environment.Environment(g, 0)
for t in range(g.time_horizon):
strategy = agent.compute_strategy()
e.observe_strategy(strategy)
realization = agent.sample_strategy()
e.observe_realization(realization)
feedback = e.feedback("expert")
agent.receive_feedback(feedback)
for i in g.history:
print(i)
#! /usr/bin/python2
import source.game as game
if __name__ == "__main__":
game = game.Game()
game.on_loop()