def make_experiment(exp_id=1,
path="./Results/Temp/{domain}/{agent}/{representation}/",
boyan_N0=136,
lambda_=0.0985,
initial_learn_rate=0.090564,
resolution=13.,
num_rbfs=9019):
opt = {}
opt["path"] = path
opt["exp_id"] = exp_id
opt["max_steps"] = 150000
opt["num_policy_checks"] = 30
opt["checks_per_policy"] = 1
domain = HIVTreatment()
opt["domain"] = domain
representation = NonparametricLocalBases(domain,
kernel=linf_triangle_kernel,
resolution=resolution,
normalization=True)
policy = eGreedy(representation, epsilon=0.1)
opt["agent"] = Q_LEARNING(policy,
representation,
discount_factor=domain.discount_factor,
lambda_=lambda_,
initial_learn_rate=initial_learn_rate,
learn_rate_decay_mode="boyan",
boyan_N0=boyan_N0)
experiment = Experiment(**opt)
return experiment
def make_experiment(exp_id=1,
path="./Results/Temp/{domain}/{agent}/{representation}/",
lambda_=0.9,
boyan_N0=22.36,
initial_learn_rate=.068,
discretization=9):
opt = {}
opt["path"] = path
opt["exp_id"] = exp_id
opt["max_steps"] = 150000
opt["num_policy_checks"] = 30
opt["checks_per_policy"] = 1
domain = HIVTreatment()
opt["domain"] = domain
representation = IndependentDiscretization(domain,
discretization=discretization)
policy = eGreedy(representation, epsilon=0.1)
opt["agent"] = Q_Learning(policy,
representation,
discount_factor=domain.discount_factor,
lambda_=0.9,
initial_learn_rate=initial_learn_rate,
learn_rate_decay_mode="boyan",
boyan_N0=boyan_N0)
experiment = Experiment(**opt)
return experiment
def make_experiment(exp_id=1,
path="./Results/Temp/{domain}/{agent}/{representation}/",
boyan_N0=136,
lambda_=0.0985,
initial_learn_rate=0.090564,
resolution=13.,
num_rbfs=9019):
opt = {}
opt["path"] = path
opt["exp_id"] = exp_id
opt["max_steps"] = 150000
opt["num_policy_checks"] = 30
opt["checks_per_policy"] = 1
domain = HIVTreatment()
opt["domain"] = domain
representation = RBF(domain,
num_rbfs=int(num_rbfs),
resolution_max=resolution,
resolution_min=resolution,
const_feature=False,
normalize=True,
seed=exp_id)
policy = eGreedy(representation, epsilon=0.1)
opt["agent"] = Q_LEARNING(policy,
representation,
discount_factor=domain.discount_factor,
lambda_=lambda_,
initial_learn_rate=initial_learn_rate,
learn_rate_decay_mode="boyan",
boyan_N0=boyan_N0)
experiment = Experiment(**opt)
return experiment
def initConfig(self):
domain = HIVTreatment()
kernel_resolution = 14.7920
kernel_width = (domain.statespace_limits[:, 1] - domain.statespace_limits[:, 0]) \
/ kernel_resolution
self.agentConfig['QLearning'] = {
'lambda': 0.9,
'gamma': 0.9,
'alpha': 0.08,
'alpha_decay_mode': 'boyan',
'boyan_N0': 238
}
self.agentConfig['Sarsa'] = {
'lambda': 0.9,
'gamma': 0.9,
'alpha': 0.08,
'alpha_decay_mode': 'boyan',
'boyan_N0': 238
}
self.policyConfig['eGreedy'] = {'epsilon': 0.1}
self.representationConfig['IndependentDiscretization'] = {
'discretization': 9
}
self.representationConfig['RBF'] = {
'num_rbfs': 9019,
'resolution_max': 13,
'resolution_min': 13
}
self.representationConfig['IncrementalTabular'] = {
'discretization': 35
}
self.representationConfig['KernelizediFDD'] = {
'sparsify': 1,
'kernel': gaussian_kernel,
'kernel_args': [kernel_width],
'active_threshold': 0.01,
'discover_threshold': 611850.81,
'max_active_base_feat': 10,
'max_base_feat_sim': 0.5,
'kernel_resolution': 14.7920
}
self.representationConfig['iFDD'] = {
'discretization': 18,
'discover_threshold': 107091
}
self.experimentConfig["maxSteps"] = 150000
self.experimentConfig["episodeCap"] = 200
self.experimentConfig["policyChecks"] = 30
self.experimentConfig["checksPerPolicy"] = 1
def makeComponents(self):
domain = HIVTreatment()
representation = RepresentationFactory.get(
config=self.representationConfig,
name=str(self.lstRepresentation.currentItem().text()),
domain=domain)
policy = PolicyFactory.get(config=self.policyConfig,
name=str(
self.lstPolicy.currentItem().text()),
representation=representation)
agent = AgentFactory.get(config=self.agentConfig,
name=str(self.lstAgent.currentItem().text()),
representation=representation,
policy=policy)
return domain, agent
def make_experiment(exp_id=1,
path="./Results/Temp/{domain}/{agent}/{representation}/",
discover_threshold=107091,
lambda_=0.245,
boyan_N0=514,
initial_learn_rate=.327,
discretization=18):
opt = {}
opt["path"] = path
opt["exp_id"] = exp_id
opt["max_steps"] = 150000
opt["num_policy_checks"] = 30
opt["checks_per_policy"] = 1
sparsify = 1
domain = HIVTreatment()
opt["domain"] = domain
initial_rep = IndependentDiscretization(domain,
discretization=discretization)
representation = iFDD(domain,
discover_threshold,
initial_rep,
sparsify=sparsify,
discretization=discretization,
useCache=True,
iFDDPlus=True)
#representation.PRINT_MAX_RELEVANCE = True
policy = eGreedy(representation, epsilon=0.1)
# agent = SARSA(representation,policy,domain,initial_learn_rate=initial_learn_rate,
# lambda_=.0, learn_rate_decay_mode="boyan", boyan_N0=boyan_N0)
opt["agent"] = Q_LEARNING(policy,
representation,
discount_factor=domain.discount_factor,
lambda_=lambda_,
initial_learn_rate=initial_learn_rate,
learn_rate_decay_mode="boyan",
boyan_N0=boyan_N0)
experiment = Experiment(**opt)
return experiment