def __init__(self,
task,
alpha=1.0,
gamma=0.80,
inv_temp=10.0,
stop_criterion=0.001,
mapping_prior=0.001,
goal_prior=0.001):
super(JointClusteringAgent, self).__init__(task)
self.gamma = gamma
self.inv_temp = inv_temp
self.stop_criterion = stop_criterion
self.alpha = alpha
# initialize the hypothesis space with a single hypothesis that can be augmented
# as new contexts are encountered
self.goal_hypotheses = [
GoalHypothesis(self.task.n_goals, alpha, goal_prior)
]
self.mapping_hypotheses = [
MappingHypothesis(self.task.n_primitive_actions,
self.task.n_abstract_actions, alpha,
mapping_prior)
]
# initialize the belief spaces
self.log_belief = np.zeros(1, dtype=float)
self.map_likelihood = np.zeros(1, dtype=float)
self.goal_likelihood = np.zeros(1, dtype=float)
def __init__(self,
task,
var_i=0.1,
var_e=0.1,
ucb_weight=1.0,
gamma=0.80,
inv_temp=10.0,
stop_criterion=0.001,
mapping_prior=0.001,
goal_prior=0.001):
super(FlatAgent, self).__init__(task)
self.var_i = var_i # innovation gain
self.var_e = var_e #
self.ucb_weight = ucb_weight
self.gamma = gamma
self.inv_temp = inv_temp
self.stop_criterion = stop_criterion
# initialize the hypothesis space
self.mapping_hypotheses = [
MappingHypothesis(self.task.n_primitive_actions,
self.task.n_abstract_actions, 1.0, mapping_prior)
]
# initialize the belief spaces
# self.log_belief_goal = np.ones(1, dtype=float)
self.log_belief_map = np.ones(1, dtype=float)
# initialize the q-values for each of the goals as a dictionary
self.mus = dict()
self.sigmas = dict()
self.n_goals = self.task.n_goals
def __init__(self,
task,
gamma=0.80,
inv_temp=10.0,
stop_criterion=0.001,
mapping_prior=0.001,
goal_prior=0.001,
epsilon=0.02):
super(FlatAgent, self).__init__(task)
self.gamma = gamma
self.inv_temp = inv_temp
self.stop_criterion = stop_criterion
# initialize the hypothesis space
self.goal_hypotheses = [
GoalHypothesis(self.task.n_goals, 1.0, goal_prior)
]
self.mapping_hypotheses = [
MappingHypothesis(self.task.n_primitive_actions,
self.task.n_abstract_actions, 1.0, mapping_prior)
]
# initialize the belief spaces
self.log_belief_goal = np.ones(1, dtype=float)
self.log_belief_map = np.ones(1, dtype=float)
def __init__(self,
task,
lr=0.1,
gamma=0.80,
inv_temp=10.0,
stop_criterion=0.001,
mapping_prior=0.001,
goal_prior=0.001):
super(NoCTX_QLearningAgent, self).__init__(task)
self.lr = lr
self.gamma = gamma
self.inv_temp = inv_temp
self.stop_criterion = stop_criterion
# initialize the hypothesis space
self.mapping_hypotheses = [
MappingHypothesis(self.task.n_primitive_actions,
self.task.n_abstract_actions, 1.0, mapping_prior)
]
# initialize the belief spaces
# self.log_belief_goal = np.ones(1, dtype=float)
self.log_belief_map = np.ones(1, dtype=float)
# initialize the q-values for each of the goals as a dictionary
self.q = np.ones(self.task.n_goals, dtype=float) / float(
self.task.n_goals)
self.n_goals = self.task.n_goals