reward = reward_step
if stepIdx > 100:
s, a, r = PG.store_transition(observation_step, action,
reward)
if stepIdx % 6 == 0 and stepIdx > 100:
PG.learn()
for k in range(len(observation)):
ss = observation[k].copy()
ss.extend(matrixOfChanAlloc.copy().reshape(
1, nOfenb * nOfchannel).tolist()[0])
# print(ss)
observation_step = np.array(ss).reshape(
nOfenb * nOfchannel + 4, 1).ravel()
print("observation_step: ", observation_step)
if observation_step[1] > 0:
action = PG.choose_action1(observation_step,
matrixOfChanAlloc, stepIdx)
if action < 12:
action_list.append(observation_step[0])
action_list.append(observation_step[1])
action_list.append(action)
else:
action_list.append(0)
action_list.append(0)
action_list.append(0)
reward = 0
if k == len(observation) - 1 or observation[k + 1][1] == 0:
#大step
d = ()
for b in range(len(action_list)):
d += (spaces.Discrete(int(action_list[b])), )
action_ = spaces.Tuple(d)
ss = []
for a in observation:
for b in a:
ss.append(b)
ss.extend(matrixOfChanAlloc.copy().reshape(
1, nOfenb * nOfchannel).tolist()[0]) #请求+信道占用
observation_step = np.array(ss).reshape(
nOfenb * nOfchannel + sizeperq * len(observation),
1).ravel() #变换为网络输入所要求的维度
print("observation_step: ", observation_step)
if observation_step[k * sizeperq +
1] > 0: #判断RNTI是否大于0 是否为有效请求
action = PG.choose_action1(observation_step,
matrixOfChanAlloc,
observation[k][0]) #选取动作
if action < nOfchannel: #判断是否为有效动作
observation[k][4] = action #改变状态
addaction(observation[k][0], observation[k][1],
action, action_list) #存储分配策略到action_list
else:
addaction(0, 0, 0, action_list) #空动作
reward = 0 #eposide没有结束reward为0
if stepIdx > 100 and k < numue - 1: #stepIndex大于100开始进入学习过程，开始存储信息
s, a, r = PG.store_transition(
observation_step,
action + observation[k][0] * nOfchannel, reward)