def test_should_form_action_set(self, cfg):
# given
population = ClassifiersList(cfg=cfg)
c0 = Classifier(action=0, cfg=cfg)
c01 = Classifier(action=0, cfg=cfg)
c1 = Classifier(action=1, cfg=cfg)
population.append(c0)
population.append(c01)
population.append(c1)
# when & then
action_set = ClassifiersList.form_action_set(population, 0, cfg)
assert 2 == len(action_set)
assert c0 in action_set
assert c01 in action_set
# when & then
action_set = ClassifiersList.form_action_set(population, 1, cfg)
assert 1 == len(action_set)
assert c1 in action_set
def test_should_form_action_set(self, cfg):
# given
cl_1 = Classifier(action=0, cfg=cfg)
cl_2 = Classifier(action=0, cfg=cfg)
cl_3 = Classifier(action=1, cfg=cfg)
population = ClassifiersList(*[cl_1, cl_2, cl_3])
action = 0
# when
action_set = ClassifiersList.form_action_set(population, action)
# then
assert len(action_set) == 2
assert cl_1 in action_set
assert cl_2 in action_set
match_set = ClassifiersList.form_match_set(population, state, cfg)
if moves > 0:
action_set.apply_alp(prev_state, action, state,
ALL_MOVES + moves, population, match_set)
action_set.apply_reinforcement_learning(
reward, match_set.get_maximum_fitness())
if cfg.do_ga:
action_set.apply_ga(ALL_MOVES + moves, population,
match_set, state)
# Determine best action
action = match_set.choose_action(cfg.epsilon)
action_set = ClassifiersList.form_action_set(
match_set, action, cfg)
prev_state = state
state, reward, done, debug = board.step(action)
if done:
action_set.apply_alp(prev_state, action, state,
ALL_MOVES + moves, population, None)
action_set.apply_reinforcement_learning(reward, 0)
if cfg.do_ga:
action_set.apply_ga(ALL_MOVES + moves, population, None,
state)
if g % 10 == 0:
print_metrics(g, ALL_MOVES, population)
