def set_mu_from_states(self, s, seed=1, n_samples_eval=6000):
if hasattr(self, "Pi"):
del self.Pi
self.mu, self.mu_r, self.mu_next, self.mu_phi, self.mu_phi_next = mdp.samples_distribution_from_states(self.mdp, policy=self.target_policy, phi=self.phi, states=s[:n_samples_eval, :],
n_next=self.mu_n_next,
seed=seed)
print "Mu set to trajectory samples"
def set_mu_from_states(self, s, seed=1, n_samples_eval=6000):
if hasattr(self, "Pi"):
del self.Pi
self.mu, self.mu_r, self.mu_next, self.mu_phi, self.mu_phi_next = mdp.samples_distribution_from_states(self.mdp, policy=self.target_policy, phi=self.phi, states=s[:n_samples_eval, :],
n_next=self.mu_n_next,
seed=seed)
print "Mu set to trajectory samples"
def set_mu_from_trajectory(self, n_samples=1000, n_eps=1,
verbose=0, seed=1, n_samples_eval=6000):
s, _, _, _, restarts = self.mdp.samples_cached(n_iter=n_samples,
n_restarts=n_eps,
policy=self.behavior_policy,
seed=seed, verbose=verbose)
if hasattr(self, "Pi"):
del self.Pi
self.mu, self.mu_r, self.mu_next, self.mu_phi, self.mu_phi_next = mdp.samples_distribution_from_states(self.mdp, policy=self.target_policy, phi=self.phi, states=s[:n_samples_eval, :],
n_next=self.mu_n_next,
seed=self.mu_seed)
print "Mu set to trajectory samples"
def set_mu_from_trajectory(self, n_samples=1000, n_eps=1,
verbose=0, seed=1, n_samples_eval=6000):
s, _, _, _, restarts = self.mdp.samples_cached(n_iter=n_samples,
n_restarts=n_eps,
policy=self.behavior_policy,
seed=seed, verbose=verbose)
if hasattr(self, "Pi"):
del self.Pi
self.mu, self.mu_r, self.mu_next, self.mu_phi, self.mu_phi_next = mdp.samples_distribution_from_states(self.mdp, policy=self.target_policy, phi=self.phi, states=s[:n_samples_eval, :],
n_next=self.mu_n_next,
seed=self.mu_seed)
print "Mu set to trajectory samples"
