def __init__(self):
self.Num_AP = 1
self.Num_UE = 50
self.channel = [1]
self.oriTHO = np.zeros([1, self.Num_AP])
loss_cal = env.Scenario(self.Num_AP, self.Num_UE, freq=2, avr_ap=1)
self.contact, self.placeAP, self.placeUE, self.Loss = loss_cal.sendout(
)
self.action_space = spaces.Box(low=-0.2,
high=0.2,
shape=(self.Num_AP, ),
dtype=np.float32)
self.observation_space = spaces.Box(low=-0,
high=1,
shape=(self.Num_AP, ),
dtype=np.float32)
self.state = self.Num_AP * [0.5]
envir = tho.ThoughtOutCal(self.channel, self.state * 60, self.Num_AP,
self.Num_UE)
RSSI, Speed, self.connection = envir.subspeed(self.Loss)
thought_out_ue, P = envir.thomain(Speed, self.connection)
# 将UE的转化为AP的
thought_out_AP = np.zeros([self.Num_AP])
for kki in range(0, self.Num_AP):
tempN = np.argwhere(self.connection == kki)
for kkj in tempN:
thought_out_AP[kki] += thought_out_ue[kkj]
self.oriTHO[:] = thought_out_AP[:]
def step(self, u):
reward = np.zeros([1, self.Num_AP])
s_ = np.zeros([self.Num_AP])
for kk in range(0, self.Num_AP):
if self.state[kk] + u[kk] < 0:
s_[kk] = 0
elif self.state[kk] + u[kk] > 1:
s_[kk] = 1
else:
s_[kk] = self.state[kk] + u[kk]
envir = tho.ThoughtOutCal(self.channel, s_ * 60, self.Num_AP,
self.Num_UE)
RSSI, Speed, connection = envir.subspeed(self.Loss)
thought_out_ue, P = envir.thomain(Speed, connection)
# 将UE的转化为AP的
thought_out_AP = np.zeros([self.Num_AP])
for kki in range(0, self.Num_AP):
tempN = np.argwhere(connection == kki)
for kkj in tempN:
thought_out_AP[kki] += thought_out_ue[kkj]
# 计算reward
for kk in range(0, self.Num_AP):
if self.state[kk] + u[kk] < 0:
reward[kk] = -100
elif self.state[kk] + u[kk] > 1:
reward[kk] = -100
else:
tempppppp = thought_out_AP[kk]
reward[kk] = tempppppp * 10
# reward[kk] = (thought_out_AP[kk]-self.oriTHO[kk])*10
self.oriTHO[:] = thought_out_AP[:]
# print(s_.shape)
return s_, np.sum(reward), False, {}