def _compute(self, tolerance, verbose=False):
start_time = time.time()
num_optimization_steps = 1000
value_functions = avf_values(
self.env,
num_optimization_steps=num_optimization_steps,
num_tasks=self.num_tasks)
value_functions = np.squeeze(value_functions)
value_functions = np.atleast_2d(value_functions)
self.metric = np.zeros((self.num_states, self.num_states))
for s in range(self.num_states):
# We take advantage of symmetry for faster computation.
for t in range(s + 1, self.num_states):
max_difference = 0.0
for policy in range(value_functions.shape[0]):
for a in range(self.num_actions):
q1 = (self.env.rewards[s, a] + self.env.gamma *
np.matmul(self.env.transition_probs[s, a, :],
value_functions[policy, :]))
q2 = (self.env.rewards[t, a] + self.env.gamma *
np.matmul(self.env.transition_probs[t, a, :],
value_functions[policy, :]))
action_diff = abs(q1 - q2)
if action_diff > max_difference:
max_difference = action_diff
self.metric[s, t] = max_difference
self.metric[t, s] = max_difference
# We don't really have a sampled versiion of this.
total_time = time.time() - start_time
self.statistics = metric.Statistics(0., total_time,
num_optimization_steps, 0.)
def _compute(self, tolerance, verbose=False):
del tolerance
assert self.env.q_values is not None, 'Q-Values have not been computed.'
self.metric = np.max(np.abs(self.env.q_values[:, None, :] -
self.env.q_values[None, :, :]),
axis=-1)
self.statistics = metric.Statistics(0., 0., 0, 0.)
def _compute(self, tolerance, verbose=False):
"""Compute exact/online lax-bisimulation metric up to specified tolerance.
Args:
tolerance: float, maximum difference in metric estimate between successive
iterations. Once this threshold is past, computation stops.
verbose: bool, whether to print verbose messages.
"""
# Initial metric is all zeros.
curr_metric = np.zeros((self.num_states, self.num_states))
metric_difference = tolerance * 2.
i = 1
exact_metric_differences = []
start_time = time.time()
while metric_difference > tolerance:
new_metric = np.zeros((self.num_states, self.num_states))
state_action_metric = np.zeros((self.num_states, self.num_actions,
self.num_states, self.num_actions))
for s in range(self.num_states):
for t in range(self.num_states):
for a in range(self.num_actions):
for b in range(self.num_actions):
next_state_distrib_1 = self.env.transition_probs[s, a, :]
next_state_distrib_2 = self.env.transition_probs[t, b, :]
rew1 = self.env.rewards[s, a]
rew2 = self.env.rewards[t, b]
emd = ot.emd2(
next_state_distrib_1, next_state_distrib_2, curr_metric)
state_action_metric[s, a, t, b] = (
abs(rew1 - rew2) + self.gamma * emd)
# Now that we've updated the state-action metric, we compute the Hausdorff
# metric.
for s in range(self.num_states):
for t in range(s + 1, self.num_states):
# First we find \sup_x\inf_y d(x, y) from Definition 5 in paper.
max_a = None
for a in range(self.num_actions):
min_b = np.min(state_action_metric[s, a, t, :])
if max_a is None or min_b > max_a:
max_a = min_b
# Next we find \sup_y\inf_x d(x, y) from Definition 5 in paper.
max_b = None
for b in range(self.num_actions):
min_a = np.min(state_action_metric[s, :, t, b])
if max_b is None or min_a > max_b:
max_b = min_a
new_metric[s, t] = max(max_a, max_b)
new_metric[t, s] = new_metric[s, t]
metric_difference = np.max(abs(new_metric - curr_metric))
exact_metric_differences.append(metric_difference)
if verbose:
logging.info('Iteration %d: %f', i, metric_difference)
curr_metric = np.copy(new_metric)
i += 1
total_time = time.time() - start_time
self.metric = curr_metric
self.statistics = metric.Statistics(
tolerance, total_time, i, exact_metric_differences)
def _compute(self, tolerance, verbose=False):
"""Compute exact/online bisimulation metric up to the specified tolerance.
Args:
tolerance: float, maximum difference in metric estimate between successive
iterations. Once this threshold is past, computation stops.
verbose: bool, whether to print verbose messages.
"""
# Initial metric is all zeros.
curr_metric = np.zeros((self.num_states, self.num_states))
metric_difference = tolerance * 2.
i = 1
exact_metric_differences = []
start_time = time.time()
while metric_difference > tolerance:
new_metric = np.zeros((self.num_states, self.num_states))
for s in range(self.num_states):
for t in range(self.num_states):
for a in range(self.num_actions):
next_state_distrib_1 = self.env.transition_probs[s,
a, :]
next_state_distrib_2 = self.env.transition_probs[t,
a, :]
rew1 = self.env.rewards[s, a]
rew2 = self.env.rewards[t, a]
emd = ot.emd2(next_state_distrib_1,
next_state_distrib_2, curr_metric)
act_distance = abs(rew1 - rew2) + self.gamma * emd
if act_distance > new_metric[s, t]:
new_metric[s, t] = act_distance
metric_difference = np.max(abs(new_metric - curr_metric))
exact_metric_differences.append(metric_difference)
if verbose:
logging.info('Iteration %d: %f', i, metric_difference)
curr_metric = np.copy(new_metric)
i += 1
total_time = time.time() - start_time
self.metric = curr_metric
self.statistics = metric.Statistics(tolerance, total_time, i,
exact_metric_differences)
def _compute(self, tolerance, verbose=False):
"""Compute the bisimulation relation and convert it to a discrete metric.
Args:
tolerance: float, unused.
verbose: bool, whether to print verbose messages.
Returns:
Statistics object containing statistics of computation.
"""
del tolerance
equivalence_classes_changing = True
iteration = 0
start_time = time.time()
# All states start in the same equivalence class.
equivalence_classes = [list(range(self.num_states))]
state_to_class = [0] * self.num_states
while equivalence_classes_changing:
equivalence_classes_changing = False
class_removed = False
iteration += 1
new_equivalence_classes = copy.deepcopy(equivalence_classes)
new_state_to_class = copy.deepcopy(state_to_class)
for s1 in range(self.num_states):
if self._state_matches_class(
s1, equivalence_classes[state_to_class[s1]]):
continue
# We must find a new class for s1.
equivalence_classes_changing = True
previous_class = new_state_to_class[s1]
new_state_to_class[s1] = -1
# Checking if there are still any elements in s1's old class.
potential_new_class = [
x for x in new_equivalence_classes[previous_class] if x != s1]
if potential_new_class:
new_equivalence_classes[previous_class] = potential_new_class
else:
# remove s1's old class from the list of new_equivalence_classes.
new_equivalence_classes.pop(previous_class)
class_removed = True
# Re-index the classes.
for i, c in enumerate(new_state_to_class):
if c > previous_class:
new_state_to_class[i] = c - 1
for i, c in enumerate(new_equivalence_classes):
if not class_removed and i == previous_class:
continue
if self._state_matches_class(s1, c):
new_state_to_class[s1] = i
new_equivalence_classes[i] += [s1]
break
if new_state_to_class[s1] < 0:
# If we haven't found a matching equivalence class, we create a new
# one.
new_equivalence_classes.append([s1])
new_state_to_class[s1] = len(new_equivalence_classes) - 1
equivalence_classes = copy.deepcopy(new_equivalence_classes)
state_to_class = copy.deepcopy(new_state_to_class)
if iteration % 1000 == 0 and verbose:
tf.logging.info('Iteration {}'.format(iteration))
# Now that we have the equivalence classes, we create the metric.
self.metric = np.ones((self.num_states, self.num_states))
for c in equivalence_classes:
for s1 in c:
for s2 in c:
self.metric[s1, s2] = 0.
total_time = time.time() - start_time
self.statistics = metric.Statistics(-1., total_time, iteration, 0.0)