def test_should_find_suitable_classifier(self, cfg):
# given
cfg.theta_r = 0.5
population = ClassifiersList()
prev_situation = Perception('01100000')
situation = Perception('11110000')
act = 0
# C1 - OK
c1 = Classifier(condition='0##0####', action=0, effect='1##1####',
quality=0.7, cfg=cfg)
# C2 - wrong action
c2 = Classifier(condition='0##0####', action=1, effect='1##1####',
quality=0.7, cfg=cfg)
# C3 - wrong condition
c3 = Classifier(condition='0##1####', action=0, effect='1##1####',
quality=0.7, cfg=cfg)
# C4 - wrong effect
c4 = Classifier(condition='0##0####', action=0, effect='1##0####',
quality=0.7, cfg=cfg)
# C5 - wrong quality
c5 = Classifier(condition='0##0####', action=0, effect='1##1####',
quality=0.25, cfg=cfg)
population.append(c2)
population.append(c3)
population.append(c4)
population.append(c5)
# when
result0 = suitable_cl_exists(population,
p0=prev_situation,
p1=situation, action=act)
population.append(c1)
result1 = suitable_cl_exists(population,
p0=prev_situation,
p1=situation, action=act)
# then
assert result0 is False
assert result1 is True
def _run_action_planning(self,
env,
time: int,
state: Perception,
prev_state: Perception,
action_set: ClassifiersList,
action: int,
last_reward: int) -> Tuple[int, Perception,
Perception,
ClassifiersList,
int, int]:
"""
Executes action planning for model learning speed up.
Method requests goals from 'goal generator' provided by
the environment. If goal is provided, ACS2 searches for
a goal sequence in the current model (only the reliable classifiers).
This is done as long as goals are provided and ACS2 finds a sequence
and successfully reaches the goal.
Parameters
----------
env
time
state
prev_state
action_set
action
last_reward
Returns
-------
steps
state
prev_state
action_set
action
last_reward
"""
logging.debug("** Running action planning **")
if not hasattr(env.env, "get_goal_state"):
logging.debug("Action planning stopped - "
"no function get_goal_state in env")
return 0, state, prev_state, action_set, action, last_reward
steps = 0
done = False
while not done:
goal_situation = self.cfg.environment_adapter.to_genotype(
env.env.get_goal_state())
if goal_situation is None:
break
act_sequence = search_goal_sequence(self.population, state,
goal_situation)
# Execute the found sequence and learn during executing
i = 0
for act in act_sequence:
if act == -1:
break
match_set = self.population.form_match_set(state)
if action_set is not None and len(prev_state) != 0:
ClassifiersList.apply_alp(
self.population,
match_set,
action_set,
prev_state,
action,
state,
time + steps,
self.cfg.theta_exp,
self.cfg)
ClassifiersList.apply_reinforcement_learning(
action_set,
last_reward,
0,
self.cfg.beta,
self.cfg.gamma)
if self.cfg.do_ga:
ClassifiersList.apply_ga(
time + steps,
self.population,
match_set,
action_set,
state,
self.cfg.theta_ga,
self.cfg.mu,
self.cfg.chi,
self.cfg.theta_as,
self.cfg.do_subsumption,
self.cfg.theta_exp)
action = act
action_set = ClassifiersList.form_action_set(match_set, action)
iaction = self.cfg.environment_adapter.to_lcs_action(action)
raw_state, last_reward, done, _ = env.step(iaction)
prev_state = state
state = self.cfg.environment_adapter.to_genotype(raw_state)
state = Perception(state)
if not suitable_cl_exists(action_set, prev_state,
action, state):
# no reliable classifier was able to anticipate
# such a change
break
steps += 1
i += 1
if i == 0:
break
return steps, state, prev_state, action_set, action, last_reward