def test(times):
data_saver = DataSaver("qlearning_test")
data_saver.add_item("start_time", time.strftime("%Y-%m-%d_%H-%M-%S"))
from src.envs.ec.ec_env import ECMA
env = ECMA()
RL = QLearningTable()
data_saver.add_item("qlearning_param",RL.param)
data_saver.add_item("episodes",times)
for episode in range(0, times):
env.reset()
observation = env.get_obs()
observation = list(np.round(np.array(observation,dtype=float).flatten(), 3))
while True:
action = RL.choose_action(str(observation))
refined_action = RL.refine_action(action)
reward, done ,_ = env.step(refined_action)
observation_ = env.get_obs()
observation_ = list(np.round(np.array(observation_,dtype=float).flatten(), 3))
observation = copy.deepcopy(observation_)
data_saver.append(str(episode),[observation,refined_action,reward])
if done:
if episode / 1000 > 0:
print(episode, time.strftime("%Y-%m-%d_%H-%M-%S"))
break
data_saver.add_item("end_time", time.strftime("%Y-%m-%d_%H-%M-%S"))
data_saver.to_file()
def train(times=10000):
data_saver = DataSaver("qlearning_training")
data_saver.add_item("start_time", time.strftime("%Y-%m-%d_%H-%M-%S"))
#创建环境
env = create_env()
RL = QLearningTable()
data_saver.add_item("RL_param",RL.param)
data_saver.add_item("episodes",times)
for episode in range(0, times):
env.reset()
observation = env.get_obs()
observation = list(np.round(np.array(observation,dtype=float).flatten(), 3))
while True:
action = RL.choose_action(str(observation))
refined_action = RL.refine_action(action)
reward, done ,_ = env.step(refined_action)
observation_ = env.get_obs()
observation_ = list(np.round(np.array(observation_,dtype=float).flatten(), 3))
RL.learn(str(observation), action, reward, str(observation_),done)
observation = copy.deepcopy(observation_)
data_saver.append(str(episode),[observation,refined_action,reward])
if done:
if episode / 1000 > 0:
print(episode, time.strftime("%Y-%m-%d_%H-%M-%S"))
if episode / 10000:
RL.save_model()
break
data_saver.add_item("end_time", time.strftime("%Y-%m-%d_%H-%M-%S"))
data_saver.to_file()