if __name__ == "__main__":
if len(sys.argv) > 1:
algo = Algo()
while True:
#need to set Beta[accel] to 1
#algo.updateBeta([0,1,0,0])
#algo.updateBetaFinal([0,1,0,0])
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
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)
ret = gameStrat(strat = 6, totalgames=50,Algo=algo, \
renderme = True, env = env)
algo.update_ygradient(ret['perf'])
algo.update_ymisc(ret['loss'])
#update algo
updateB = algo.eval(l=0.5, logbestrun=True, logbestrunloss=True)
algo.updateBetaFinal(updateB)
logit(logMt=(ind, algo))
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)
ret = gameStrat(strat = 6, totalgames=50,Algo=algo, \
renderme = True, env = env)
algo.update_ygradient(ret['perf'])
algo.update_ymisc(ret['loss'])
#update algo
updateB = algo.eval(l = 0.5, logbestrun=True, logbestrunloss=True)
algo.updateBetaFinal(updateB)
from learning import Algo a = Algo() ret = a.permute_variables( [[0,0,0,0]], [ (1,(-.1,.1)), (2,(-2,1)) ] ) print 'test: ', str(ret) print 'testlen: ', str(len(ret)) ret = a.build_gradient(vars = [1,2]) print 'testlen: ', str(len(ret)) ret = a.build_gradient(vars = [1,2], no_origin = True) print 'test: ', str(ret) print 'testlen: ', str(len(ret)) ret = a.build_gradient(vars = [1,2,3,4], no_origin = True) print 'testlen: ', str(len(ret))
# options, arguments = p.parse_args()
if __name__ == "__main__":
if len(sys.argv) > 1:
algo = Algo()
while True:
#need to set Beta[accel] to 1
#algo.updateBeta([0,1,0,0])
#algo.updateBetaFinal([0,1,0,0])
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
from learning import Algo a = Algo() ret = a.permute_variables([[0, 0, 0, 0]], [(1, (-.1, .1)), (2, (-2, 1))]) print 'test: ', str(ret) print 'testlen: ', str(len(ret)) ret = a.build_gradient(vars=[1, 2]) print 'testlen: ', str(len(ret)) ret = a.build_gradient(vars=[1, 2], no_origin=True) print 'test: ', str(ret) print 'testlen: ', str(len(ret)) ret = a.build_gradient(vars=[1, 2, 3, 4], no_origin=True) print 'testlen: ', str(len(ret))
