def _compute_almost_sure_reachability(self):
objective = AS_REACH
# removed stores reloads that had been removed from the MDP
removed = set()
# Initialize the helper values
self.reach_safe = [inf] * self.states
# Initialized safe values after reaching T
self.get_safe()
safe_after_T = lambda s: self.safe_values[
s] if s in self.targets else inf
done = False
while not done:
### 2.1.1.
# safe_after_T initializes each iteration of the fixpoint with:
# * self.safe_values[t] for targets (reach done, just survive)
# * ∞ for the rest
self._sufficient_levels(self.reach_safe, removed, safe_after_T)
### 2.1.2. Initialize PosReach_M:
# * safe_value for target states
# * inf otherwise
self.alsure_values = [inf] * self.states
for t in self.targets:
self.alsure_values[t] = self.get_safe()[t]
self._init_strategy(objective)
### 2.1.3 Compute PosReach on sub-MDP
# Mitigate reload removal (use Safe_M for survival)
rem_survival_val = lambda s: self.reach_safe[s]
rem_action_value = lambda a, v: self._action_value_T(
a, v, rem_survival_val)
# Avoid unnecesarry computations
is_removed = lambda x: x in removed # always ∞
is_target = lambda x: x in self.targets # always Safe
skip_cond = lambda x: is_removed(x) or is_target(x)
## Finish the fixpoint
largest_fixpoint(self.mdp,
self.alsure_values,
rem_action_value,
value_adj=self._reload_capper,
skip_state=skip_cond,
on_update=self._update_function(objective))
### 2.2. & 2.3. Detect bad reloads and remove them
done = True
# Iterate over reloads and remove unusable ones (∞)
for s in range(self.states):
if self.is_reload(s) and self.alsure_values[s] == inf:
if s not in removed:
removed.add(s)
done = False
self._copy_strategy(SAFE, objective, self.targets)
def _compute_buchi(self):
objective = BUCHI
# removed stores reloads that had been removed from the MDP
removed = set()
# Initialize the helper values
self.buchi_safe = [inf] * self.states
done = False
while not done:
### 1.1. Compute Safe(M\removed) and store it in buchi_safe
self._sufficient_levels(self.buchi_safe, removed, objective=HELPER)
### 1.2. Initialization of PosReach_M
# * buchi_safe for target states
# * inf otherwise
self.buchi_values = [inf] * self.states
for t in self.targets:
self.buchi_values[t] = self.buchi_safe[t]
self._init_strategy(objective)
### 1.3. Computation of PosReach on sub-MDP (with removed reloads)
## how much do I need to survive via this state after reloads removal
# Use Safe_m (stored in buchi_safe) as value needed for survival
rem_survival_val = lambda s: self.buchi_safe[s]
# Navigate towards T and survive with Safe_m
rem_action_value = lambda a, v: self._action_value_T(
a, v, rem_survival_val)
## Avoid unnecessary computations
is_removed = lambda x: x in removed # always ∞
is_target = lambda x: x in self.targets # always Safe_M
skip_cond = lambda x: is_removed(x) or is_target(x)
## Finish the fixpoint
largest_fixpoint(self.mdp,
self.buchi_values,
rem_action_value,
value_adj=self._reload_capper,
skip_state=skip_cond,
on_update=self._update_function(objective))
### 2. & 3. Detect bad reloads and remove them
done = True
# Iterate over reloads and remove unusable ones (∞)
for s in range(self.states):
if self.is_reload(s) and self.buchi_values[s] == inf:
if s not in removed:
removed.add(s)
done = False
self._copy_strategy(HELPER, objective, self.targets)
def _compute_minInitCons(self):
objective = MIN_INIT_CONS
self._init_strategy(objective)
self.mic_values = [inf] * self.states
cap = lambda s, v: inf if v > self.cap else v
largest_fixpoint(self.mdp,
self.mic_values,
self._action_value,
value_adj=cap,
on_update=self._update_function(objective))
def _compute_positive_reachability(self):
objective = POS_REACH
self._init_strategy(objective)
# Initialize:
# * safe_value for target states
# * inf otherwise
self.get_safe()
self.pos_reach_values = [inf] * self.states
for t in self.targets:
self.pos_reach_values[t] = self.safe_values[t]
largest_fixpoint(
self.mdp,
self.pos_reach_values,
self._action_value_T,
value_adj=self._reload_capper,
# Target states are always safe_values[t]
skip_state=lambda x: x in self.targets,
on_update=self._update_function(objective))
if self.compute_strategy:
self._copy_strategy(SAFE, objective, self.targets)
def _sufficient_levels(self,
values=None,
removed=None,
init_val=lambda s: inf,
objective=HELPER):
"""Compute the safe_values using the largest-fixpoint method
based on minInitCons computation with removal of reload states
that have minInitCons() = ∞ in the previous itertions.
The first computation computes, in fact, minInitCons (redundantly)
The worst-case complexity is |R| * minInitCons = |R|*|S|^2
`values` : list used for computation
self.safe_values as default
`removed` : set of reloads - start with the reloads already removed
∅ by default
`init_val`: state -> value - defines the values at the start of each
iteration of inner fixpoint (currently needed only for)
almost-sure reachability. It simulates switching between
the MDP with 2 sets of reload states (one before, one
[the original set R] after reaching T).
"""
if values is None:
values = self.safe_values
if removed is None:
removed = set()
done = False
while not done:
# Compute fixpoint without removed reloads
self._init_strategy(objective)
# Reset the computation, by default set all values to ∞.
# `init_val: s -> v
for s in range(self.states):
values[s] = init_val(s)
# Mitigate reload removal
zero_cond = lambda x: self.is_reload(x) and x not in removed
rem_action_value = lambda a, v: self._action_value(a, v, zero_cond)
# Removed reloads are skipped
skip_cond = lambda x: x in removed # Improve performance only
# Over capacity values -> ∞
cap = lambda s, v: inf if v > self.cap else v
largest_fixpoint(self.mdp,
values,
rem_action_value,
value_adj=cap,
skip_state=skip_cond,
on_update=self._update_function(objective))
done = True
# Iterate over reloads and remove unusable ones (∞)
for s in range(self.states):
if self.is_reload(s) and values[s] == inf:
if s not in removed:
removed.add(s)
done = False
# Set reload values to 0, "< & +1"-trick to handle ∞
for s in range(self.states):
if self.mdp.is_reload(s) and values[s] < self.cap + 1:
values[s] = 0