def __init__(self,
algorithm='qlearning',
policy="eps_greedy",
nA=10,
nS=4**4,
lvfa=False,
feature_size=13,
alpha=0.01,
gamma=0.99,
eligibility=True,
episodes=1000,
load_model=None,
**kwargs):
self.algorithm = Algorithm(algorithm=algorithm,
nS=nS,
nA=nA,
lvfa=lvfa,
feature_size=feature_size,
eligibility=eligibility,
alpha=alpha,
gamma=gamma,
**kwargs)
self.policy = Policy(policy=policy,
episodes=episodes,
nA=nA,
lvfa=lvfa,
**kwargs)
from models import Elit
from utils.converter import Converter
from utils.operators.basic import Operator as op
from utils.operators.modified import Operator as op_m
from utils.policies import Policy
from utils.test_functions import levi
import numpy as np
converter = Converter(type_='grey')
crossover = op.crossover()
mutator = op_m.Mutate.multi(3)
policies = {
'include': Policy.elitarium(),
'exclude': Policy.elitarium(),
'parents': Policy.random()
}
n = 100
eps = 1
model = Elit(converter=converter,
policies=policies,
mutator=mutator,
crossover=crossover)
model.run(*(levi()),
epochs=101,
n=20,
eps=eps,
optimize='min',
t=10,