def main(args):
if args.verbose:
logging.basicConfig(level=logging.INFO)
elif args.debug:
logging.basicConfig(level=logging.DEBUG)
# initializations
gridworld = GridWorld(args.size, args.interval, args.obstacles, args.vision, args.phase)
logging.info("Generated grid world!")
logging.info("Visuals created")
mc = MonteCarlo(gridworld, mode=args.method)
logging.info("Initialized Monte Carlo method")
mc.run()
for s in self.q:
for a in [Action.HIT, Action.STICK]:
self.q[s][a] += alpha * delta * self.E[s][a]
self.E[s][a] = Sarsa.GAMMA * self._lambda * self.E[s][a]
if __name__ == "__main__":
g = graph.graphxy(width=30,
x=graph.axis.linear(min=100, max=1000),
y=graph.axis.linear(),
key=graph.key.key(pos="bl"))
plots = []
""" Re-calculate V* """
m = MonteCarlo()
for i in range(1, 50000):
m.run()
for _l in [e / 10.0 for e in range(0, 11, 1)]:
print("Training Sarsa(%s)" % _l)
s = Sarsa(_l)
c1 = []
c2 = []
for j in range(1, 1001):
s.run()
if j % 100 == 0:
c1.append(j)
e = s.mean_squared_error(m.q)
c2.append(e)
if j % 100 == 0:
print("Error = %2f" % e)
title = "Sarsa(%s)" % _l
L2 = L * L
xL2 = (x - L) * (x - L)
yL2 = (y - L) * (y - L)
c1 = x2 + y2 <= L2
c2 = xL2 + y2 <= L2
c3 = x2 + yL2 <= L2
c4 = xL2 + yL2 <= L2
tc = c1 and c2 and c3 and c4
return 1 if tc else 0
mc = MonteCarlo(seed, samples, target_function_L, 2)
print(mc.run())
def target_function(random_vector):
'''
Function receives a uniform random vector in [0]
'''
x = random_vector[0]
y = random_vector[1]
c1 = x + y < 1
return 1 if c1 else 0
samples = 10000
mc = MonteCarlo(seed, samples, target_function, 2)
print(mc.run())
