def __init__(self,
rewards_locations,
obstacles_location,
initial_state=None,
obstacles_transition_probability=.2,
success_probability=.9):
if obstacles_transition_probability < 0 or obstacles_transition_probability > 1:
raise ValueError(
'obstacles_transition_probability must be in range [0, 1]')
self.num_states = 64
self.reward_location = rewards_locations
self.initial_state = initial_state
self.transition_probabilities = np.ones(
self.num_states) * success_probability
self.transition_probabilities[
obstacles_location] = obstacles_transition_probability
self.graph = graphs.LowStretchTree(k=3)
self.weighted_graph = graphs.LowStretchTree(k=3)
for obstacle in obstacles_location:
self.weighted_graph.W[
obstacle, :] *= obstacles_transition_probability
self.weighted_graph.W[:,
obstacle] *= obstacles_transition_probability
self.weighted_graph.Ne = sparse.tril(self.weighted_graph.W).nnz
self._state = self._init_random_state()
def test_LowStretchTree():
G = graphs.LowStretchTree()
def test_lowstretchtree(self):
graphs.LowStretchTree()
def test_LowStretchTree():
G = graphs.LowStretchTree()
needed_attributes_testing(G)