def prob(p):
p = float(p)
n = 1.0 - p
return {
'S': (('S', p), ('N', n)),
'N': (('N', p), ('S', n)),
'E': (('E', p), ('W', n)),
'W': (('W', p), ('E', n)),
}
STATES = ((0, 3), (0, 2), (1, 2), (0, 1), (1, 1), (0, 0))
if __name__ == '__main__':
print "\nInitial values:"
g = GridWorld(GRID, prob(1), STATES, 1, -4)
print g
i = g.value_iteration(0.1)
print "\nValues after %d iterations:" % i
print g
g = GridWorld(GRID, prob(0.8), STATES, 1, -4)
print "\nValue of (1,2) after first iteration: %.1f" % g.value(0, 3)
g = GridWorld(GRID, prob(0.8), STATES, 1, -4)
i = g.value_iteration(0.1)
print "\nValues after %d iterations:" % i
print g
from MDP.grid import GridWorld
GRID = [[0, 0, 0, 0], [-100, 0, 0, 100]]
def prob(p):
p = float(p)
n = 1.0 - p
return {"S": (("S", p), ("N", n)), "N": (("N", p), ("S", n)), "E": (("E", p), ("W", n)), "W": (("W", p), ("E", n))}
STATES = ((0, 3), (0, 2), (1, 2), (0, 1), (1, 1), (0, 0))
if __name__ == "__main__":
print "\nInitial values:"
g = GridWorld(GRID, prob(1), STATES, 1, -4)
print g
i = g.value_iteration(0.1)
print "\nValues after %d iterations:" % i
print g
g = GridWorld(GRID, prob(0.8), STATES, 1, -4)
print "\nValue of (1,2) after first iteration: %.1f" % g.value(0, 3)
g = GridWorld(GRID, prob(0.8), STATES, 1, -4)
i = g.value_iteration(0.1)
print "\nValues after %d iterations:" % i
print g
from MDP.grid import GridWorld
GRID = [[0, 0, 0, 100],
[0, None, 0, -100],
[0, 0, 0, 0]]
PROB = {
'S':(('S', 0.8), ('W', 0.1), ('E', 0.1)),
'N':(('N', 0.8), ('E', 0.1), ('W', 0.1)),
'E':(('E', 0.8), ('S', 0.1), ('N', 0.1)),
'W':(('W', 0.8), ('N', 0.1), ('S', 0.1)),
}
STATES = ((0,2),(0,1),(1,2),(0,0),(2,2),(1,0),(2,1),(2,0),(2,3))
if __name__ == '__main__':
g = GridWorld(GRID, PROB, STATES, 1, -3)
v = g.value(0,2)
print "\nValue of (0,2) after first iteration: %.1f" % v
g.grid[0][2] = v
print "\nValue of (1,2) after first iteration: %.1f" % g.value(1,2)
print "\nInitial values:"
g = GridWorld(GRID, PROB, STATES, 1, -3)
print g
i = g.value_iteration(0.1)
print "\nValues after %d iterations:" % i
print g
).satisfiability_report()
print Proposition(
lambda a, b, c: (implies(a, b) or implies(b, c)) == implies(a, c),
"((a => b) and (b => c)) == (a => c)"
).satisfiability_report()
print "\n=== Problem 14 ==="
from MDP.grid import GridWorld
GRID = [[0, 0, None, 100],
[0, 0, 0, 0]]
PROB = {
'S':(('S', 1.0), ),
'N':(('N', 1.0), ),
'E':(('E', 1.0), ),
'W':(('W', 1.0), ),
}
STATES = ((1,3),(1,2),(1,1),(0,1),(1,0),(0,0))
g = GridWorld(GRID, PROB, STATES, 1, -5)
i = g.value_iteration(0.1)
print "Values after %d iterations:" % i
print g
print "\n=== Problem 15 ==="
from Markov.markov import TransProb
TRANSITIONS = [("A","A","A","A","B")]
t = TransProb(TRANSITIONS)
t.report(k=1)
print Proposition(lambda a, b, c: implies(a, b) and (not ((not a) or b)),
"(a => b) and (not ((not a) or b))").satisfiability_report()
print Proposition(
lambda a, b, c: (implies(a, b) or implies(b, c)) == implies(a, c),
"((a => b) and (b => c)) == (a => c)").satisfiability_report()
print "\n=== Problem 14 ==="
from MDP.grid import GridWorld
GRID = [[0, 0, None, 100], [0, 0, 0, 0]]
PROB = {
'S': (('S', 1.0), ),
'N': (('N', 1.0), ),
'E': (('E', 1.0), ),
'W': (('W', 1.0), ),
}
STATES = ((1, 3), (1, 2), (1, 1), (0, 1), (1, 0), (0, 0))
g = GridWorld(GRID, PROB, STATES, 1, -5)
i = g.value_iteration(0.1)
print "Values after %d iterations:" % i
print g
print "\n=== Problem 15 ==="
from Markov.markov import TransProb
TRANSITIONS = [("A", "A", "A", "A", "B")]
t = TransProb(TRANSITIONS)
t.report(k=1)
