95 lines
2.7 KiB
Python
95 lines
2.7 KiB
Python
import numpy as np
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from GEO_parameters import GEO_Parameters
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import torch
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class GEOEnv:
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def __init__(self, num_satellite = 1, num_carrier_wave = 100, num_beam = 1, num_user_dim = 32, delt_t =5, dataset = "month_2.txt", max_intervals = 8):
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self.action_space = num_carrier_wave
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self.user_space_dim = num_user_dim
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self.user_space = num_user_dim * num_user_dim
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self.delt_t = delt_t
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self.beams = num_beam
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self.satellite = num_satellite
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self.steps_before_termination = 0
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self.datas = []
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self.dataset = dataset
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self.num_data = len(self.datas)
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self.state = np.zeros((self.action_space+1, self.user_space_dim, self.user_space_dim), dtype=np.uint8)
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self.max_intervals = 8
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def load_data(self):
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with open(self.dataset, 'r') as f:
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self.datas = f.readlines()
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self.num_data = len(self.datas)
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def compute_state(self, index):
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# state = self.state.copy()
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self.state[-1] = np.zeros_like(self.state[-1])
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[date_t, beam_type, user, ap] = self.datas[index].split('\t')
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user = int(user)
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ap = int(ap)
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beam_type = int(beam_type)
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i = user//10
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j = user%10
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t = 0
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if ap == 0:
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t = beam_type
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else:
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t = -beam_type
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self.state[-1][i][j] = t
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return self.state
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def compute_reward(self, action):
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assert type(action) == int
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assert action >= 0 and action < self.action_space
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# 功率暂时不用
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# 频谱效率
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axis2_sum = np.sum(self.state, axis= 2)
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matrix_sum = np.sum(axis2_sum, axis=1)
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if (matrix_sum)
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t = 0.0
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for i in range(self.action_space):
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if matrix_sum[i] == 0:
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t += 1/self.action_space
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else:
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t += 1/self.action_space * matrix_sum[i]/self.max_intervals
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# 用户满意率，即action所在的信道的最大可用带宽的满足程度
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b = 0.0
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acquire = matrix_sum[-1]
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supply = self.max_intervals - matrix_sum[action]
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b = min(1.0, acquire/supply)
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# 如果用户被拒绝了会有一个非常大的惩罚
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if abs(b) < 1e-7 or supply <= 0:
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b = -1.0
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# 载波共用的惩罚
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c:float = 0
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# 暂时不动
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return t + b
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def renew_environment():
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pass
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def step(self, action):
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self.state = self.compute_state(self.steps_before_termination)
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reward = self.compute_reward(action)
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# self.pre
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if __name__ == "__main__":
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e = GEOEnv()
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e.load_data()
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print(len(e.datas))
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state = e.compute_state(0)
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print(state)
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state[1][1][2] = 4
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t = e.compute_reward(state, 1)
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print(t)
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