def stacking_assign_q_learning(shorter_init, longer_init):
env = Stacking(shorter_init, longer_init)
RL = QLearningTable(actions=list(range(6)), e_greedy=1)
if shorter_init[0] == 'A' and longer_init[0] == 'U':
RL.q_table = RL.q_table.append(q_table_A_U)
elif shorter_init[0] == 'C' and longer_init[0] == 'G':
RL.q_table = RL.q_table.append(q_table_C_G)
elif shorter_init[0] == 'G' and longer_init[0] == 'C':
RL.q_table = RL.q_table.append(q_table_G_C)
elif shorter_init[0] == 'G' and longer_init[0] == 'U':
RL.q_table = RL.q_table.append(q_table_G_U)
elif shorter_init[0] == 'U' and longer_init[0] == 'A':
RL.q_table = RL.q_table.append(q_table_U_A)
elif shorter_init[0] == 'U' and longer_init[0] == 'G':
RL.q_table = RL.q_table.append(q_table_U_G)
observation = env.shorter + "_" + env.longer
while True:
action = RL.choose_action(observation)
shorter_, longer_, reward, done = env.step(action)
observation_ = shorter_ + "_" + longer_
# RL.learn(str(observation), action, reward, str(observation_))
observation = observation_
if done:
break
shorter_final = observation.split('_')[0]
longer_final = observation.split('_')[1]
return shorter_final, longer_final
def ubp_6_assign_q_learning(shorter_init):
env = ubp_6(shorter_init)
RL = QLearningTable(actions=list(range(4)), e_greedy=1)
RL.q_table = RL.q_table.append(q_table_ubp_6)
observation = env.shorter
while True:
action = RL.choose_action(observation)
shorter_, reward, done = env.step(action)
observation_ = shorter_
# RL.learn(str(observation), action, reward, str(observation_))
observation = observation_
if done:
break
return observation