def make_experiment(exp_id=1,
path="./Results/Temp/{domain}/{agent}/{representation}/",
boyan_N0=2120,
initial_learn_rate=.26,
lambda_=0.9,
resolution=8,
num_rbfs=4958):
opt = {}
opt["path"] = path
opt["exp_id"] = exp_id
opt["max_steps"] = 30000
opt["num_policy_checks"] = 20
opt["checks_per_policy"] = 10
domain = FiniteCartPoleBalanceModern()
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 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}/",
boyan_N0=235,
initial_learn_rate=.05,
discretization=5):
opt = {}
opt["exp_id"] = exp_id
opt["max_steps"] = 30000
opt["num_policy_checks"] = 20
opt["checks_per_policy"] = 1
domain = FiniteCartPoleBalanceOriginal(good_reward=0.)
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 make_experiment(exp_id=1,
path="./Results/Temp/{domain}/{agent}/{representation}/",
discover_threshold=1e-20,
boyan_N0=1589.56,
lambda_=0.52738,
initial_learn_rate=.0124409,
kernel_resolution=8.5):
opt = {}
opt["path"] = path
opt["exp_id"] = exp_id
opt["max_steps"] = 100000
opt["num_policy_checks"] = 10
opt["checks_per_policy"] = 100
active_threshold = 0.01
max_base_feat_sim = 0.7
sparsify = 10
domain = PuddleGapWorld()
opt["domain"] = domain
kernel_width = old_div(
(domain.statespace_limits[:, 1] - domain.statespace_limits[:, 0]),
kernel_resolution)
representation = KernelizediFDD(domain,
sparsify=sparsify,
kernel=gaussian_kernel,
kernel_args=[kernel_width],
active_threshold=active_threshold,
discover_threshold=discover_threshold,
normalization=False,
max_active_base_feat=100,
max_base_feat_sim=max_base_feat_sim)
# policy = UniformRandom(representation, )
policy = eGreedy(representation, epsilon=0.1)
# agent = SARSA(representation,policy,domain,initial_learn_rate=1.,
# lambda_=0., learn_rate_decay_mode="boyan", boyan_N0=100)
opt["agent"] = Q_LEARNING(policy,
representation,
discount_factor=domain.discount_factor,
lambda_=lambda_,
initial_learn_rate=initial_learn_rate,
learn_rate_decay_mode="boyan_const",
boyan_N0=boyan_N0)
stat_bins_per_state_dim = 22
experiment = Experiment(**opt)
return experiment
def make_experiment(exp_id=1,
path="./Results/Temp/{domain}/{agent}/{representation}/",
discover_threshold=.21,
boyan_N0=37.,
lambda_=.9,
initial_learn_rate=.07,
kernel_resolution=13.14):
opt = {}
opt["path"] = path
opt["exp_id"] = exp_id
opt["max_steps"] = 30000
opt["num_policy_checks"] = 20
opt["checks_per_policy"] = 10
active_threshold = 0.01
max_base_feat_sim = 0.5
sparsify = 1
domain = FiniteCartPoleBalanceOriginal(good_reward=0.)
opt["domain"] = domain
# domain = FiniteCartPoleBalanceModern()
kernel_width = old_div(
(domain.statespace_limits[:, 1] - domain.statespace_limits[:, 0]),
kernel_resolution)
representation = KernelizediFDD(domain,
sparsify=sparsify,
kernel=gaussian_kernel,
kernel_args=[kernel_width],
active_threshold=active_threshold,
discover_threshold=discover_threshold,
normalization=True,
max_active_base_feat=10,
max_base_feat_sim=max_base_feat_sim)
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
def make_experiment(exp_id=1,
path="./Results/Temp/{domain}/{agent}/{representation}/",
discover_threshold=8.925,
boyan_N0=840.,
lambda_=0.5203,
initial_learn_rate=.7512,
kernel_resolution=26.4777):
opt = {}
opt["path"] = path
opt["exp_id"] = exp_id
opt["max_steps"] = 40000
opt["num_policy_checks"] = 20
opt["checks_per_policy"] = 100
active_threshold = 0.01
max_base_feat_sim = 0.5
sparsify = 1
domain = PuddleWorld()
opt["domain"] = domain
kernel_width = old_div(
(domain.statespace_limits[:, 1] - domain.statespace_limits[:, 0]),
kernel_resolution)
representation = KernelizediFDD(domain,
sparsify=sparsify,
kernel=linf_triangle_kernel,
kernel_args=[kernel_width],
active_threshold=active_threshold,
discover_threshold=discover_threshold,
normalization=True,
max_active_base_feat=10,
max_base_feat_sim=max_base_feat_sim)
policy = eGreedy(representation, epsilon=0.1)
# agent = SARSA(representation,policy,domain,initial_learn_rate=1.,
# lambda_=0., learn_rate_decay_mode="boyan", boyan_N0=100)
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=.02208,
lambda_=0.6756,
boyan_N0=480.72,
initial_learn_rate=.2911,
kernel_resolution=18.435):
opt = {}
opt["exp_id"] = exp_id
opt["max_steps"] = 10000
opt["num_policy_checks"] = 20
opt["checks_per_policy"] = 10
active_threshold = 0.01
max_base_feat_sim = 0.5
sparsify = 1
domain = InfCartPoleBalance()
opt["domain"] = domain
kernel_width = (domain.statespace_limits[:, 1] -
domain.statespace_limits[:, 0]) / kernel_resolution
representation = KernelizediFDD(domain,
sparsify=sparsify,
kernel=linf_triangle_kernel,
kernel_args=[kernel_width],
active_threshold=active_threshold,
discover_threshold=discover_threshold,
normalization=True,
max_active_base_feat=10,
max_base_feat_sim=max_base_feat_sim)
policy = eGreedy(representation, epsilon=0.1)
# agent = SARSA(representation,policy,domain,initial_learn_rate=1.,
# lambda_=0., learn_rate_decay_mode="boyan", boyan_N0=100)
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=.01256,
lambda_=0.81,
boyan_N0=9811.2337,
initial_learn_rate=.15,
discretization=22.):
opt = {}
opt["path"] = path
opt["exp_id"] = exp_id
opt["max_steps"] = 10000
opt["num_policy_checks"] = 20
opt["checks_per_policy"] = 10
sparsify = True
domain = InfCartPoleBalance()
opt["domain"] = domain
initial_rep = IndependentDiscretization(domain,
discretization=discretization)
representation = iFDD(domain,
discover_threshold,
initial_rep,
sparsify=sparsify,
discretization=discretization,
useCache=True,
iFDDPlus=True)
policy = eGreedy(representation, epsilon=0.1)
# agent = SARSA(representation,policy,domain,initial_learn_rate=1.,
# lambda_=0., learn_rate_decay_mode="boyan", boyan_N0=100)
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=.0807,
boyan_N0=389.56,
lambda_=0.52738,
initial_learn_rate=.424409,
kernel_resolution=8.567677):
opt = {}
opt["exp_id"] = exp_id
opt["max_steps"] = 40000
opt["num_policy_checks"] = 20
opt["checks_per_policy"] = 100
active_threshold = 0.01
max_base_feat_sim = 0.5
sparsify = 1
domain = PuddleWorld()
opt["domain"] = domain
kernel_width = (domain.statespace_limits[:, 1] -
domain.statespace_limits[:, 0]) / kernel_resolution
representation = KernelizediFDD(domain,
sparsify=sparsify,
kernel=gaussian_kernel,
kernel_args=[kernel_width],
active_threshold=active_threshold,
discover_threshold=discover_threshold,
normalization=True,
max_active_base_feat=10,
max_base_feat_sim=max_base_feat_sim)
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=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
def make_experiment(exp_id=1,
path="./Results/Temp/{domain}/{agent}/{representation}/",
discover_threshold=77.,
boyan_N0=11,
lambda_=0.9,
initial_learn_rate=.05,
discretization=47):
opt = {}
opt["exp_id"] = exp_id
opt["max_steps"] = 30000
opt["num_policy_checks"] = 20
opt["checks_per_policy"] = 10
sparsify = 1
domain = FiniteCartPoleBalanceOriginal(good_reward=0.)
opt["domain"] = domain
initial_rep = IndependentDiscretization(domain,
discretization=discretization)
representation = iFDD(domain,
discover_threshold,
initial_rep,
sparsify=sparsify,
discretization=discretization,
useCache=True,
iFDDPlus=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
