def test_should_apply_reinforcement_learning(self, cfg):
# given
cl = Classifier(reward=34.29, immediate_reward=11.29, cfg=cfg)
population = ClassifiersList(*[cl])
# when
ClassifiersList.apply_reinforcement_learning(population, 0, 28.79,
cfg.beta, cfg.gamma)
# then
assert abs(33.94 - cl.r) < 0.1
assert abs(10.74 - cl.ir) < 0.1
def test_should_apply_reinforcement_learning(self, cfg):
# given
population = ClassifiersList(cfg=cfg)
c1 = Classifier(cfg=cfg)
c1.r = 34.29
c1.ir = 11.29
population.append(c1)
# when
population.apply_reinforcement_learning(0, 28.79)
# then
assert abs(33.94 - population[0].r) < 0.1
assert abs(10.74 - population[0].ir) < 0.1
# Play some games
for g in range(GAMES):
action_set = ClassifiersList(cfg=cfg)
prev_state, action, reward, done = None, None, None, False
state = board.reset()
moves = 0
while not done:
player = determine_player(moves) # Determine player
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,
