def __init__(self,
policy,
mdp_info,
learning_rate,
sigma_learning_rate=None,
update_mode='deterministic',
update_type='weighted',
init_values=(0., 500.),
delta=0.1,
minimize_wasserstein=True):
super(GaussianDoubleQLearning,
self).__init__(policy, mdp_info, learning_rate,
sigma_learning_rate, update_mode, update_type,
init_values, delta, minimize_wasserstein)
self.Qs = [
EnsembleTable(2, mdp_info.size),
EnsembleTable(2, mdp_info.size)
]
for i in range(len(self.Qs[0])):
self.Qs[0][i].table = np.tile([init_values[i]], self.Q[i].shape)
for i in range(len(self.Qs[1])):
self.Qs[1][i].table = self.Qs[0][i].table.copy()
self.Q[i].table = self.Qs[0][i].table.copy()
self.alpha = [deepcopy(self.alpha), deepcopy(self.alpha)]
def __init__(self,
policy,
mdp_info,
learning_rate,
n_approximators=10,
mu=0.,
sigma=1.,
p=1.,
cross_update=False):
super(BootstrappedDoubleQLearning,
self).__init__(policy, mdp_info, learning_rate, n_approximators,
mu, sigma, p, cross_update)
self.Qs = [
EnsembleTable(n_approximators, mdp_info.size),
EnsembleTable(n_approximators, mdp_info.size)
]
for i in range(len(self.Qs[0])):
self.Qs[0][i].table = np.random.randn(
*self.Qs[0][i].shape) * self._sigma + self._mu
for i in range(len(self.Qs[1])):
self.Qs[1][i].table = self.Qs[0][i].table.copy()
self.Q[i].table = self.Qs[0][i].table.copy()
self.alpha = [deepcopy(self.alpha), deepcopy(self.alpha)]
def __init__(self,
policy,
mdp_info,
learning_rate,
sigma_learning_rate=None,
sigma_1_learning_rate=None,
update_mode='deterministic',
update_type='weighted',
init_values=[0., 0., 500.],
delta=0.1,
q_max=None,
minimize_wasserstein=True,
clip_variance=True):
self._update_mode = update_mode
self._update_type = update_type
self.delta = delta
self.n_approximators = len(init_values)
self.Q = EnsembleTable(len(init_values), mdp_info.size)
if q_max is None:
q_max = 1 / (1 - mdp_info.gamma)
if self.n_approximators == 3:
q_max = init_values[0]
self.sigma_b = init_values[-1]
self.q_max = q_max
for i in range(len(self.Q.model)):
self.Q.model[i].table = np.tile([init_values[i]], self.Q[i].shape)
super(Gaussian, self).__init__(self.Q, policy, mdp_info, learning_rate)
if sigma_learning_rate is None:
sigma_learning_rate = deepcopy(learning_rate)
self.alpha = [
deepcopy(self.alpha),
deepcopy(self.alpha),
deepcopy(sigma_learning_rate)
]
self.minimize_wasserstein = minimize_wasserstein
policy = np.zeros(self.mdp_info.size)
self.standard_bound = norm.ppf(1 - self.delta, loc=0, scale=1)
for s in range(self.mdp_info.size[0]):
if self.n_approximators == 3:
means, sigmas1, sigmas2 = [x[[s]] for x in self.Q.model]
sigmas = sigmas1 + sigmas2
else:
means, sigmas = [x[[s]] for x in self.Q.model]
bounds = sigmas * self.standard_bound + means
bounds = np.clip(bounds, None, self.q_max)
actions = np.argwhere(bounds == np.max(bounds)).ravel()
n = len(actions)
for a in actions:
policy[s, a] = 1. / n
self.policy_matrix = policy
self.last_update = (0, 0)
self.clip_variance = clip_variance
if self.clip_variance:
print("***CLIPPING VARIANCE***")
def __init__(self, policy, mdp_info, learning_rate):
self.Q = EnsembleTable(2, mdp_info.size)
super().__init__(self.Q, policy, mdp_info, learning_rate)
self.alpha = [deepcopy(self.alpha), deepcopy(self.alpha)]
assert len(self.Q) == 2, 'The regressor ensemble must' \
' have exactly 2 models.'
def __init__(self, policy, mdp_info, learning_rate, n_approximators=10, update_mode='deterministic',
update_type='weighted', q_min=0, q_max=1):
super(ParticleDoubleQLearning, self).__init__(
policy, mdp_info, learning_rate, n_approximators, update_mode,
update_type, q_min, q_max
)
self.Qs = [EnsembleTable(n_approximators, mdp_info.size),
EnsembleTable(n_approximators, mdp_info.size)]
init_values = np.linspace(q_min, q_max, n_approximators)
for i in range(len(self.Qs[0])):
self.Qs[0][i].table =np.tile([init_values[i]], self.Q[i].shape)
for i in range(len(self.Qs[1])):
self.Qs[1][i].table = self.Qs[0][i].table.copy()
self.Q[i].table = self.Qs[0][i].table.copy()
self.alpha = [deepcopy(self.alpha), deepcopy(self.alpha)]
def __init__(self, policy, mdp_info, learning_rate, n_approximators=10, update_mode='deterministic',
update_type='weighted', q_min=0, q_max=1):
self._n_approximators = n_approximators
self._update_mode = update_mode
self._update_type = update_type
self.Q = EnsembleTable(self._n_approximators, mdp_info.size)
init_values = np.linspace(q_min, q_max, n_approximators)
for i in range(len(self.Q.model)):
self.Q.model[i].table = np.tile([init_values[i]], self.Q[i].shape)
super(Particle, self).__init__(self.Q, policy, mdp_info,
learning_rate)
self.alpha = [deepcopy(self.alpha)] * n_approximators
def __init__(self, policy, mdp_info, learning_rate, n_approximators=10,
mu=0., sigma=1., p=2 / 3., cross_update=False):
self._n_approximators = n_approximators
self._mu = mu
self._sigma = sigma
self._p = p
self._cross_update = cross_update
self._mask = np.random.binomial(1, self._p, self._n_approximators)
self.Q = EnsembleTable(self._n_approximators, mdp_info.size)
for i in range(len(self.Q.model)):
self.Q.model[i].table = np.random.randn(
*self.Q[i].shape) * self._sigma + self._mu
super(Bootstrapped, self).__init__(self.Q, policy, mdp_info,
learning_rate)
self.alpha = [deepcopy(self.alpha)] * n_approximators
def __init__(self, policy, mdp_info, learning_rate, n_approximators=10, update_mode='deterministic',
update_type='weighted', q_min=0, q_max=1, init_values=None, delta =0.1):
self._n_approximators = n_approximators
self._update_mode = update_mode
self._update_type = update_type
self.delta = delta
self.quantiles = [i * 1. / (n_approximators - 1) for i in range(n_approximators)]
for p in range(n_approximators):
if self.quantiles[p] >= 1 - delta:
self.delta_index = p
break
self.Q = EnsembleTable(self._n_approximators, mdp_info.size)
if init_values is None:
init_values = np.linspace(q_min, q_max, n_approximators)
for i in range(len(self.Q.model)):
self.Q.model[i].table = np.tile([init_values[i]], self.Q[i].shape)
super(Particle, self).__init__(self.Q, policy, mdp_info,
learning_rate)
self.alpha = [deepcopy(self.alpha)] * n_approximators
