def create_rps_game(n=1, eps=0):
assert n >= 1
k = n - 1
delta = 60 / (6 * n)
# TODO
context = {
'xRock': create_rock(k=k, p1=True, delta=delta, eps=eps),
'xScissors': create_scissors(k=k, p1=True, delta=delta, eps=eps),
'xPaper': create_paper(k=k, p1=True, delta=delta, eps=eps),
'yRock': create_rock(k=k, p1=False, delta=delta),
'yScissors': create_scissors(k=k, p1=False, delta=delta),
'yPaper': create_paper(k=k, p1=False, delta=delta),
}
context = fn.walk_keys(stl.parse, context)
context.update(CONTEXT)
spec = G.Specs(
obj=stl.parse('G(Rules)', H=H).inline_context(context),
learned=stl.TOP,
init=stl.parse("Init").inline_context(context),
)
return G.Game(specs=spec, model=MODEL)
def create_game(n=2, eps=0):
assert n >= 1
k = n - 1
delta = 2 * 60 / n + 1e-2 # add padding to make last interval closed.
# TODO
p1_moves = gen_moves(k, p1=True, delta=delta, eps=eps)
p2_moves = gen_moves(k, p1=False, delta=delta)
def create_rule(i):
return parse(f'(yMove{(i + 1) % n}) -> (~(xMove{i}))')
context = {parse(f'xMove{i}'): move for i, move in enumerate(p1_moves)}
context.update(
{parse(f'yMove{i}'): move
for i, move in enumerate(p2_moves)})
context.update({
parse("Init"): parse('(x = 0) & (y = 0)'),
parse('Rules'): stl.andf(*map(create_rule, range(n))),
})
spec = G.Specs(
obj=parse('G(Rules)').inline_context(context),
learned=stl.TOP,
init=parse("Init").inline_context(context),
)
return G.Game(specs=spec, model=MODEL)
from magnum import game as G
import numpy as np
# Setup the Model
H = 8
model = G.Model(
dt=0.1,
vars=G.Vars(state=("x1", "x2"), input=("u", ), env=("w1", "w2")),
dyn=G.Dynamics(
A=np.array([[0, 1], [-1, -2]]),
B=np.array([[20], [0]]),
C=np.array([[1, 0], [0, 1]])))
# Setup the specification
context = {
stl.parse("Init"): stl.parse("(x1 = 4.2) & (x2 = 0)"),
stl.parse("Sys"): stl.parse("F[0, 3]((x1 >= 5) & (x1 <= 6))", H=1),
}
spec = G.Specs(
obj=stl.parse("Sys").inline_context(context),
init=stl.parse("Init").inline_context(context),
learned=stl.TOP,
)
g = G.Game(specs=spec, model=model)
import stl
from magnum import game as G
import numpy as np
# Setup the Model
H = 3
model = G.Model(dt=1,
vars=G.Vars(state=("x", ), input=("u", ), env=()),
dyn=G.Dynamics(A=np.array([[0]]),
B=np.array([[10]]),
C=np.array([[]])))
# Setup the specification
context = {
stl.parse("Init"):
stl.parse("x = 3"),
stl.parse("ReachFive"):
stl.parse("(F(x > 5)) & (G((x > 5) -> (F[0,2](x < 3))))", H=H),
}
spec = G.Specs(
obj=stl.parse("ReachFive").inline_context(context),
init=stl.parse("Init").inline_context(context),
learned=stl.TOP,
)
feasible_example = G.Game(specs=spec, model=model)