algo.reset_ygradient()
for point in algo.build_gradient(vars = var):
algo.updateBeta(point)
perf = gameStrat(strat = 6, totalgames=3,Algo=algo, \
renderme = False)
y = evalGames(perf)
algo.update_ygradient(y)
#update algo
updateB = algo.eval()
algo.updateBetaFinal(updateB)
#Exit Training Conditions
if y > 500:
break
#Print summary of loop outcome
print str(algo.BetaFinal)
else:
for s in [1,2,3,4]:
perf = gameStrat(strat = s, totalgames=5)
eval = evalGames(perf)
print eval
#import optparse
# p = optparse.OptionParser()
# p.add_option('--foo', '-f', default="yadda")
# p.add_option('--bar', '-b')
# options, arguments = p.parse_args()
if __name__ == "__main__":
if len(sys.argv) > 1:
algo = Algo()
#cheats, init-Betas
algo.updateBetaFinal([0,0,0,.1])
env = createEnvNoisy()
ind = 0
while True:
var = [0,1,2,3]
ep = [0.2,0.1,.005,0.01]
algo.reset_ygradient()
algo.reset_ymisc()
for point in algo.build_gradient(vars = var, eps = ep):
algo.updateBeta(point)
return 0
#import optparse
# p = optparse.OptionParser()
# p.add_option('--foo', '-f', default="yadda")
# p.add_option('--bar', '-b')
# options, arguments = p.parse_args()
if __name__ == "__main__":
if len(sys.argv) > 1:
algo = Algo()
#cheats, init-Betas
algo.updateBetaFinal([0, 0, 0, .1])
env = createEnvNoisy()
ind = 0
while True:
var = [0, 1, 2, 3]
ep = [0.2, 0.1, .005, 0.01]
algo.reset_ygradient()
algo.reset_ymisc()
for point in algo.build_gradient(vars=var, eps=ep):
algo.updateBeta(point)
var = [2]
algo.reset_ygradient()
for point in algo.build_gradient(vars=var):
algo.updateBeta(point)
perf = gameStrat(strat = 6, totalgames=3,Algo=algo, \
renderme = False)
y = evalGames(perf)
algo.update_ygradient(y)
#update algo
updateB = algo.eval()
algo.updateBetaFinal(updateB)
#Exit Training Conditions
if y > 500:
break
#Print summary of loop outcome
print str(algo.BetaFinal)
else:
for s in [1, 2, 3, 4]:
perf = gameStrat(strat=s, totalgames=5)
eval = evalGames(perf)
print eval