def makeComponents(self):
self.block_number = int(self.spinBoxBlocksNumber.value())
noise = float(self.spNoise.value())
domain = BlocksWorld(blocks=self.block_number,
towerSize=self.block_number,
noise=noise)
domain.GOAL_REWARD = float(self.spGoalReward.value())
domain.STEP_REWARD = float(self.spStepReward.value())
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=0.03104970,
lambda_=0.,
boyan_N0=1220.247254,
initial_learn_rate=0.27986823):
opt = {}
opt["exp_id"] = exp_id
opt["max_steps"] = 100000
opt["num_policy_checks"] = 20
opt["checks_per_policy"] = 1
sparsify = 1
ifddeps = 1e-7
domain = BlocksWorld(blocks=6, noise=0.3, )
opt["domain"] = domain
initial_rep = IndependentDiscretization(domain)
representation = iFDD(domain, discover_threshold, initial_rep,
sparsify=sparsify,
useCache=True,
iFDDPlus=1 - ifddeps)
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}/",
discover_threshold=0.012695,
lambda_=0.2,
boyan_N0=80.798,
initial_learn_rate=0.402807):
opt = {}
opt["exp_id"] = exp_id
opt["path"] = path
opt["max_steps"] = 100000
opt["num_policy_checks"] = 20
opt["checks_per_policy"] = 1
sparsify = 1
domain = BlocksWorld(blocks=6, noise=0.3)
opt["domain"] = domain
initial_rep = IndependentDiscretization(domain)
representation = iFDDK(domain,
discover_threshold,
initial_rep,
sparsify=sparsify,
useCache=True,
lazy=True,
lambda_=lambda_)
policy = eGreedy(representation, epsilon=0.1)
opt["agent"] = Greedy_GQ(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.,
boyan_N0=10.09,
initial_learn_rate=.47):
opt = {}
opt["exp_id"] = exp_id
opt["path"] = path
opt["max_steps"] = 100000
opt["num_policy_checks"] = 20
opt["checks_per_policy"] = 5
sparsify = 1
ifddeps = 1e-7
domain = BlocksWorld(blocks=6, noise=0.3)
opt["domain"] = domain
representation = IndependentDiscretization(domain)
policy = eGreedy(representation, epsilon=0.1)
opt["agent"] = Greedy_GQ(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 makeComponents(self):
self.block_number = int(self.spinBoxBlocksNumber.value())
noise = float(self.spNoise.value())
domain = BlocksWorld(blocks=self.block_number,towerSize=self.block_number, noise=noise)
domain.GOAL_REWARD = float(self.spGoalReward.value())
domain.STEP_REWARD = float(self.spStepReward.value())
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}/",
lambda_=0.,
boyan_N0=14.44946,
initial_learn_rate=0.240155681):
opt = {}
opt["path"] = path
opt["exp_id"] = exp_id
opt["max_steps"] = 100000
opt["num_policy_checks"] = 20
opt["checks_per_policy"] = 1
domain = BlocksWorld(
blocks=6,
noise=0.3,
)
opt["domain"] = domain
mat = np.matrix("""1 1 1 0 0 0;
0 1 1 1 0 0;
0 0 1 1 1 0;
0 0 0 1 1 1;
0 0 1 0 1 1;
0 0 1 1 0 1;
1 0 1 1 0 0;
1 0 1 0 1 0;
1 0 0 1 1 0;
1 0 0 0 1 1;
1 0 1 0 0 1;
1 0 0 1 0 1;
1 1 0 1 0 0;
1 1 0 0 1 0;
1 1 0 0 0 1;
0 1 0 1 1 0;
0 1 0 0 1 1;
0 1 0 1 0 1;
0 1 1 0 1 0;
0 1 1 0 0 1""")
#assert(mat.shape[0] == 20)
representation = TileCoding(domain,
memory=2000,
num_tilings=[1] * mat.shape[0],
resolution_matrix=mat * 6,
safety="none")
policy = eGreedy(representation, epsilon=0.1)
opt["agent"] = Greedy_GQ(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
