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env_3CAV_cooperation_20230319_benchmarks.py
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env_3CAV_cooperation_20230319_benchmarks.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Jan 17 13:32:12 2023
@author: Kaige
"""
# for 20230131-190919 and 20230201-002841
import os
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
import random
import math
import pandas as pd
import sympy as sp
from gym import spaces
##########################################################################################################################
def cal_delta(a,b,c,d):
p = (3*a*c-b*b)/(3*a*a)
q = (27*a*a*d - 9*a*b*c + 2*b*b*b)/(27*a*a*a)
delta = (q/2)*(q/2)+(p/3)*(p/3)*(p/3)
return delta
##################################################################################
class Network():
def __init__(self):
#########################################################################################################################
self.infeasible_penalty = -10
##################################### DNN model parameters ###############################################################
self.delta_1 = 120268800/30 # computing demnad (in cycles) for feature extraction
self.delta_2 = 1000/30 # computing demnad (in cycles) for feature fusion
self.delta_3 = 9255020/30 # computing demnad (in cycles) for fast inference
self.delta_4 = 2.3000e+09/30 # computing demnad (in cycles) for full inference
self.rho = 0.3; # Average early exit ratio in the default DNN model
self.rho_tilde = 0.6; # Average early exit ratio in the data-fusion DNN model
self.w = 295936; # feature data size (in bit)
self.Delta = 0.1; # Delay requirement in s, i.e., 100ms
##
self.kappa = 1e-28
self.delta = self.delta_1 + self.delta_3 + (1-self.rho) * self.delta_4
self.delta_tilde = 2*self.delta_1 + self.delta_2 + self.delta_3 + (1-self.rho_tilde) * self.delta_4
self.delta_hat = self.delta_1 + self.delta_2 + self.delta_3 + (1-self.rho_tilde) * self.delta_4
##
self.w_new = self.w/1e6*500
self.delta_hat_new = self.delta_hat/1e9*500
self.Delta_new = self.Delta*500
self.varphi = np.sqrt(2*np.power(self.delta,3)/(np.power(self.delta_hat,2)*self.delta_tilde))
############################################### Communication parameters #################################################
self.noise_power = np.power(10, -104/10)/1000 # -104dBm
self.Transmit_power = np.power(10, 23/10)/1000 # 23dBm
self.Interference_constant = 0
self.bandwidth_basis = [7,7,7,7,7,7,7,7,7,7,7,7,7,7,7,
6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,
5,5,5,5,5,5,5,5,5,5,5,5,5,5,5,
6,6,6,6,6,6,6,6,6,6,6,6,6,6,6,
7,7,7,7,7,7,7,7,7,7,7,7,7,7,7] #*1000000
self.f_center = 6 # Ghz
self.episode_length = 75
############################################### optimal resource allocation ###################################
self.obj_opt_3CAV = np.squeeze(sio.loadmat('KKT_opt/KKT_3CAV_opt_data_all.mat').get('KKT_obj_3CAV_all'))
self.obj_opt_2CAV = np.squeeze(sio.loadmat('KKT_opt/KKT_2CAV_opt_data.mat').get('KKT_obj_2CAV'))
self.obj_opt_1CAV = np.squeeze(sio.loadmat('KKT_opt/KKT_1CAV_opt_data.mat').get('KKT_obj_1CAV'))
# self.obj_opt_3CAV = np.zeros([6,5,5,5,10,10,10])
# self.obj_opt_2CAV = np.zeros([6,5,5,10,10])
# self.obj_opt_1CAV = np.zeros([6,5,10])
############################################### CAV environment ###############################################
self.CAV_pairs = 2
self.n_adversaries = 0
self.action_space = [spaces.Discrete(2) for i in range(self.CAV_pairs)]
# self.observation_space = [spaces.Box(5,) for i in range(self.n)]
self.observation_space = [(6,) for i in range(self.CAV_pairs)] # bandwidth, own workload, own distance, avg workload of others, average distance of others
self.distances = np.arange(6,36,6) # All candidate states for transmitter-receiver distances
# state transition prob. matrix for distance states
self.trans_matrix = np.array([[0.35, 0.30, 0.20, 0.10, 0.05],
[0.25, 0.30, 0.25, 0.15, 0.05],
[0.10, 0.25, 0.30, 0.25, 0.10],
[0.05, 0.15, 0.25, 0.30, 0.25],
[0.05, 0.10, 0.20, 0.30, 0.35]])
# self.trans_matrix = np.array([[0.9, 0.1, 0, 0, 0],
# [0.05, 0.9, 0.05, 0, 0],
# [0, 0.05, 0.9, 0.05, 0],
# [0, 0, 0.05, 0.9, 0.05],
# [0, 0, 0, 0.1, 0.9]])
self.workloads = np.array([4,5,6,7,8])
self.load_trans = np.array([[0.35, 0.30, 0.20, 0.10, 0.05],
[0.25, 0.30, 0.25, 0.15, 0.05],
[0.10, 0.25, 0.30, 0.25, 0.10],
[0.05, 0.15, 0.25, 0.30, 0.25],
[0.05, 0.10, 0.20, 0.30, 0.35]])
# self.workloads = np.array([4,5,6,7,8])
# self.load_trans = np.array([[0.55, 0.24, 0.12, 0.06, 0.03],
# [0.16, 0.575, 0.16, 0.07, 0.035],
# [0.07, 0.14, 0.58, 0.14, 0.07],
# [0.035, 0.07, 0.16, 0.575, 0.16],
# [0.03, 0.06, 0.12, 0.24, 0.55]])
# print(np.sum(self.load_trans,axis=1))
# self.load_trans = np.array([[0.9, 0.1, 0, 0, 0],
# [0.05, 0.9, 0.05, 0, 0],
# [0, 0.05, 0.9, 0.05, 0],
# [0, 0, 0.05, 0.9, 0.05],
# [0, 0, 0, 0.1, 0.9]])
self.O_init = 8
def reset(self):
self.step_count = 0
####################### Available bandwidth (depending on HDV status) #####################################################
band_basis = self.bandwidth_basis[self.step_count]
if band_basis == 7:
self.Bandwidth_available = np.random.default_rng().choice([6,7],1, [0.5, 0.5])
elif band_basis == 2:
self.Bandwidth_available = np.random.default_rng().choice([2,3],1, [0.5, 0.5])
else:
self.Bandwidth_available = np.random.default_rng().choice([band_basis-1, band_basis, band_basis+1], 1, [0.25, 0.5, 0.25])
Bandwidth_norm = (self.Bandwidth_available - 2)/5
########################### CAV Workload (number of objects for detection) ##################################################################
# Dynmaic workload: number of objects for detection by the DNN model
self.O_vehs_all = self.O_init * np.ones(self.CAV_pairs)
O_norm = (self.O_vehs_all - np.array([4,4,4]))/4
############################# Distance and channel gain ###############################################
self.Distance_all = 6*np.ones(self.CAV_pairs)
Distance_norm = (self.Distance_all - np.array([6,6,6]))/24
# Path_loss_dB_all = 32.4 + 20*np.log10(self.Distance_all) + 20*math.log10(self.f_center) # NR-V2X 37.885 highway case, d in meter, f_center in GhZ, Path_loss_dB in dB
# self.Channel_gain_all = 1/np.power(10, Path_loss_dB_all/10)
########################################## State ######################################################
self.curr_state = [0]*self.CAV_pairs
self.pre_action = np.array([1]*self.CAV_pairs)
self.pre_action_BFoptimal = np.array([1]*self.CAV_pairs)
self.pre_action_random = np.array([1]*self.CAV_pairs)
# print("self.pre_action_BFoptimal:",self.pre_action_BFoptimal)
for i_agent in range(0, self.CAV_pairs):
self.curr_state[i_agent] = np.r_[Bandwidth_norm, O_norm[i_agent], Distance_norm[i_agent], (sum(O_norm)-O_norm[i_agent])/(self.CAV_pairs-1),(sum(Distance_norm)-Distance_norm[i_agent])/(self.CAV_pairs-1),self.pre_action[i_agent]]
# self.curr_state[i_agent] = np.r_[Bandwidth_norm, O_norm[i_agent], Distance_norm[i_agent], (sum(O_norm)-O_norm[i_agent])/(self.n_agents-1),(sum(Distance_norm)-Distance_norm[i_agent])/(self.n_agents-1)]
# print("self.curr_state:", self.curr_state)
state = self.curr_state
self.step_count = 1
return state
def step(self, curr_action):
# switch_coeff = 0
switch_coeff = 0.4
########################################################################### Brute force optimal ######################################################################################################
# of all candidate action from 000 to 111
switch_costs_BF_optimal = np.zeros([2,2,2])
objs_BF_optimal = np.zeros([2,2,2])
rewards_BF_optimal = np.zeros([2,2,2])
for action_agent_0 in range(0,2):
for action_agent_1 in range(0,2):
for action_agent_2 in range(0,2):
actions_temp = np.array([action_agent_0, action_agent_1, action_agent_2])
# print("actions_temp:",actions_temp)
# print("self.pre_action_BFoptimal:",self.pre_action_BFoptimal)
switch_cost = self.CAV_pairs - np.count_nonzero(actions_temp == self.pre_action_BFoptimal)
# print(switch_cost)
Activated_CAV_pair_num = sum(actions_temp)
Activated_index = np.where(actions_temp == 1)
obj_opt = 0
if Activated_CAV_pair_num > 0:
O_vehs = self.O_vehs_all[Activated_index]
Distance = self.Distance_all[Activated_index]
# print("O_vehs:", O_vehs)
# print("Distance:", Distance)
if Activated_CAV_pair_num > 1:
sort_index = np.array(Distance).argsort()
# print("sort_index:", sort_index)
O_vehs_ordered = O_vehs[sort_index]
Distance_ordered = Distance[sort_index]
if Activated_CAV_pair_num == 3:
obj_opt = self.obj_opt_3CAV[int(self.Bandwidth_available-2), int(O_vehs_ordered[0]-4), int(O_vehs_ordered[1]-4), int(O_vehs_ordered[2]-4), int(Distance_ordered[0]/3-1), int(Distance_ordered[1]/3-1), int(Distance_ordered[2]/3-1)]
elif Activated_CAV_pair_num == 2:
obj_opt = self.obj_opt_2CAV[int(self.Bandwidth_available-2), int(O_vehs_ordered[0]-4), int(O_vehs_ordered[1]-4), int(Distance_ordered[0]/3-1), int(Distance_ordered[1]/3-1)]
elif Activated_CAV_pair_num == 1:
obj_opt = self.obj_opt_1CAV[int(self.Bandwidth_available-2), int(O_vehs[0]-4), int(Distance[0]/3-1)]
reward = 0
if obj_opt == -1:
reward = self.infeasible_penalty # No impact, as the brute-force will select 000, reward = 0
else:
reward = obj_opt - switch_cost*switch_coeff
switch_costs_BF_optimal[action_agent_0, action_agent_1, action_agent_2] = switch_cost
objs_BF_optimal[action_agent_0, action_agent_1, action_agent_2] = obj_opt
rewards_BF_optimal[action_agent_0, action_agent_1, action_agent_2] = reward
# print("rewards_BF_optimal:",rewards_BF_optimal)
# print("switch_costs_BF_optimal:",switch_costs_BF_optimal)
# print("objs_BF_optimal:",objs_BF_optimal)
reward_BF_optimal = np.max(rewards_BF_optimal)
# print("reward_BF_optimal:",reward_BF_optimal)
actions_BF_optimal_cand = np.where(rewards_BF_optimal==reward_BF_optimal)
# print(actions_BF_optimal_cand[0][0])
# print(actions_BF_optimal_cand[1][0])
# print(actions_BF_optimal_cand[2][0])
reward_BF_optimal = rewards_BF_optimal[actions_BF_optimal_cand]
switch_cost_BF_optimal = switch_costs_BF_optimal[actions_BF_optimal_cand]
obj_BF_optimal = objs_BF_optimal[actions_BF_optimal_cand]
actions_BF_optimal = np.array([actions_BF_optimal_cand[0][0], actions_BF_optimal_cand[1][0], actions_BF_optimal_cand[2][0]])
self.pre_action_BFoptimal = actions_BF_optimal
# print("actions_BF_optimal:",actions_BF_optimal)
# print("reward_BF_optimal:",reward_BF_optimal)
# print("switch_cost_BF_optimal:",switch_cost_BF_optimal)
# print("obj_BF_optimal:",obj_BF_optimal)
########################################################################### Random ######################################################################################################
actions_random = np.random.randint(2, size=3)
Activated_CAV_pair_num = sum(actions_random)
Activated_index = np.where(actions_random == 1)
obj_opt = 0
if Activated_CAV_pair_num > 0:
O_vehs = self.O_vehs_all[Activated_index]
Distance = self.Distance_all[Activated_index]
# print("O_vehs:", O_vehs)
# print("Distance:", Distance)
if Activated_CAV_pair_num > 1:
sort_index = np.array(Distance).argsort()
# print("sort_index:", sort_index)
O_vehs_ordered = O_vehs[sort_index]
Distance_ordered = Distance[sort_index]
if Activated_CAV_pair_num == 3:
obj_opt = self.obj_opt_3CAV[int(self.Bandwidth_available-2), int(O_vehs_ordered[0]-4), int(O_vehs_ordered[1]-4), int(O_vehs_ordered[2]-4), int(Distance_ordered[0]/3-1), int(Distance_ordered[1]/3-1), int(Distance_ordered[2]/3-1)]
elif Activated_CAV_pair_num == 2:
obj_opt = self.obj_opt_2CAV[int(self.Bandwidth_available-2), int(O_vehs_ordered[0]-4), int(O_vehs_ordered[1]-4), int(Distance_ordered[0]/3-1), int(Distance_ordered[1]/3-1)]
elif Activated_CAV_pair_num == 1:
obj_opt = self.obj_opt_1CAV[int(self.Bandwidth_available-2), int(O_vehs[0]-4), int(Distance[0]/3-1)]
reward_random = 0
if obj_opt == -1:
# reward_random = self.infeasible_penalty
actions_random = np.array([0,0,0])
obj_opt = 0
switch_cost_random = self.CAV_pairs - np.count_nonzero(actions_random == self.pre_action_random)
reward_random = obj_opt - switch_cost_random*switch_coeff
obj_random = obj_opt
self.pre_action_random = actions_random
# print("reward_random:",reward_random)
# print("switch_cost_random:",switch_cost_random)
# print("obj_random:",obj_random)
########################################################################### MADDPG ######################################################################################################
actions_array = np.array(curr_action)
# print("actions_array:", actions_array)
actions = np.array([np.argmax(actions_array[0]), np.argmax(actions_array[1]), np.argmax(actions_array[2])])
# print("actions:", actions)
################################### Action processing #################################################
# Number of CAV pairs in cooperation mode
Activated_CAV_pair_num = sum(actions)
Activated_index = np.where(actions == 1)
# Activated_index = np.array(Activated_index)[0].tolist()
# print("Activated_index:", Activated_index)
################################### Given state and action ############################################
obj_opt = 0
if Activated_CAV_pair_num > 0:
O_vehs = self.O_vehs_all[Activated_index]
Distance = self.Distance_all[Activated_index]
# print("O_vehs:", O_vehs)
# print("Distance:", Distance)
if Activated_CAV_pair_num > 1:
sort_index = np.array(Distance).argsort()
# print("sort_index:", sort_index)
O_vehs_ordered = O_vehs[sort_index]
Distance_ordered = Distance[sort_index]
if Activated_CAV_pair_num == 3:
obj_opt = self.obj_opt_3CAV[int(self.Bandwidth_available-2), int(O_vehs_ordered[0]-4), int(O_vehs_ordered[1]-4), int(O_vehs_ordered[2]-4), int(Distance_ordered[0]/3-1), int(Distance_ordered[1]/3-1), int(Distance_ordered[2]/3-1)]
elif Activated_CAV_pair_num == 2:
obj_opt = self.obj_opt_2CAV[int(self.Bandwidth_available-2), int(O_vehs_ordered[0]-4), int(O_vehs_ordered[1]-4), int(Distance_ordered[0]/3-1), int(Distance_ordered[1]/3-1)]
elif Activated_CAV_pair_num == 1:
obj_opt = self.obj_opt_1CAV[int(self.Bandwidth_available-2), int(O_vehs[0]-4), int(Distance[0]/3-1)]
####################################### Calculate the reward ##########################################
reward = 0
reward_modified = 0
if obj_opt == -1:
obj_modified = 0
actions_modified = np.array([0,0,0])
switch_cost = self.CAV_pairs - np.count_nonzero(actions_modified == self.pre_action)
reward_modified = obj_modified - switch_cost*switch_coeff
reward = reward_modified + self.infeasible_penalty
else:
obj_modified = obj_opt
actions_modified = actions
switch_cost = self.CAV_pairs - np.count_nonzero(actions_modified == self.pre_action)
reward = obj_opt - switch_cost*switch_coeff
reward_modified = reward
# print("reward_modified:",reward_modified)
# if obj_opt != -1:
# reward = obj_opt - switch_cost/10
# print("switch_cost:", switch_cost)
self.pre_action = actions_modified
# print("self.pre_action:", self.pre_action)
####################################### Get the next state ##########################################
####################### Available bandwidth (depending on HDV status) #####################################################
band_basis = self.bandwidth_basis[self.step_count]
if band_basis == 7:
self.Bandwidth_available = np.random.default_rng().choice([6,7],1, [0.5, 0.5])
elif band_basis == 2:
self.Bandwidth_available = np.random.default_rng().choice([2,3],1, [0.5, 0.5])
else:
self.Bandwidth_available = np.random.default_rng().choice([band_basis-1, band_basis, band_basis+1], 1, [0.25, 0.5, 0.25])
Bandwidth_norm = (self.Bandwidth_available - 2)/5
########################### CAV Workload (number of objects for detection) ##################################################################
O_next = np.zeros([self.CAV_pairs])
for i_agent in range(0, self.CAV_pairs):
p_O = self.load_trans[int(self.O_vehs_all[i_agent]-4),:]
O_next[i_agent] = np.random.default_rng().choice(self.workloads, 1, p=p_O)
self.O_vehs_all = O_next
O_norm = (self.O_vehs_all - np.array([4,4,4]))/4
############################# Distance and channel gain ###############################################
distance_next = np.zeros([self.CAV_pairs])
for i_agent in range(0, self.CAV_pairs):
p_D = self.trans_matrix[int(self.Distance_all[i_agent]/6-1),:]
distance_next[i_agent] = np.random.default_rng().choice(self.distances, 1, p=p_D)
self.Distance_all = distance_next
Distance_norm = (self.Distance_all - np.array([6,6,6]))/24
# print("self.O_vehs_all:", self.O_vehs_all)
# print("self.Distance_all:", self.Distance_all)
##################################### State ###########################################################
state_next = [0]*self.CAV_pairs # This is a list
rew_n = [0]*self.CAV_pairs
done_n = [0]*self.CAV_pairs
obj_n = [0]*self.CAV_pairs
switch_n = [0]*self.CAV_pairs
self.step_count += 1
done = False
if self.step_count == self.episode_length:
done = True
# self.episode += 1
for i_agent in range(0, self.CAV_pairs):
state_next[i_agent] = np.r_[Bandwidth_norm, O_norm[i_agent], Distance_norm[i_agent], (sum(O_norm)-O_norm[i_agent])/(self.CAV_pairs-1),(sum(Distance_norm)-Distance_norm[i_agent])/(self.CAV_pairs-1),self.pre_action[i_agent]]
# state_next[i_agent] = np.r_[Bandwidth_norm, O_norm[i_agent], Distance_norm[i_agent], (sum(O_norm)-O_norm[i_agent])/(self.n_agents-1),(sum(Distance_norm)-Distance_norm[i_agent])/(self.n_agents-1)]
rew_n[i_agent] = reward
done_n[i_agent] = done
obj_n[i_agent] = obj_modified
switch_n[i_agent] = switch_cost
self.curr_state = state_next # This is a list of one dimensional (4,) np arrays
return state_next, rew_n, done_n, obj_n, switch_n, reward_modified, reward_BF_optimal[0], obj_BF_optimal[0], switch_cost_BF_optimal[0], reward_random, obj_random, switch_cost_random
# ###-------------------------------
# env = Network()
# x = env.reset()
# print("Initial state:", x)
# curr_action = np.random.randint(0,2,env.CAV_pair_num)
# print("actions",curr_action)
# w, y, z = env.step(curr_action)
# print("reward:", y)
# print("state_next:", w)
# print("done:", z)
# curr_action = np.random.randint(0,2,env.CAV_pair_num)
# print("actions",curr_action)
# w, y, z = env.step(curr_action)
# print("reward:", y)
# print("state_next:", w)
# print("done:", z)
# curr_action = np.random.randint(0,2,env.CAV_pair_num)
# print("actions",curr_action)
# w, y, z = env.step(curr_action)
# print("reward:", y)
# print("state_next:", w)
# print("done:", z)
# curr_action = np.random.randint(0,2,env.CAV_pair_num)
# print("actions",curr_action)
# w, y, z = env.step(curr_action)
# print("reward:", y)
# print("state_next:", w)
# print("done:", z)