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enviornment.py
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enviornment.py
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import math
from operator import mod
from turtle import distance
import numpy as np
#import progressbar
import gym
import random
import matplotlib.pyplot as plt
## enviornment start
import matlab.engine
import csv
#eng = matlab.engine.start_matlab();
from numpy.core.fromnumeric import shape
from tensorflow.keras import Model
from tensorflow.keras.layers import Dense, Embedding, Reshape
from tensorflow.keras.optimizers import Adam
import os.path
import re
import tensorflow as tf
tf.debugging.set_log_device_placement(True)
from config import args
from scenerios import cases
#env = eng.Triangle_1.resulting();
#state = list(env._data)
#print('states [h hx hy ex ey phi m^] : ', state)
#state_space= shape(state)
#print('State_Space : ',state_space[0])
#alpha = 1.5;
#beta = 3;
#F = 2*0.001035;
#segment = (10*3.14)/180
class Enviornment:
def __init__(self, eng, args):
self.eng = eng
chosen_case = cases[args.case]
self.initial_state = np.array((chosen_case['GTO_state'])) # [h;hx;hy;ex;ey;phi;time;fuel_burnt] Initial GTO orbit state
################################################################################################
######## following initialization for dimenless quantities required for Neural network ########
self.DU = 42164; # distance unit, Km
self.TU = math.sqrt((42164**3)/398600); # time unit, s 572.92
self.SU = self.DU/self.TU; # speed unit, Km/sec
self.MU = 398600.4418; # mass Unit, Kg
self.HU = self.DU * self.SU;
self.g0 = 9.81; # m/sec^2
################################################################################################
# define path for dat files
current_dir = os.getcwd()
save_path_1 = os.path.join(current_dir, "csv_files")
self.completeName_csvlist = os.path.join(save_path_1, "csvlist.dat")
self.completeName_csvlistinitialize = os.path.join(save_path_1, "csvlistinitialize.dat")
self.completeName_Rtimeinput = os.path.join(save_path_1, "Rtimeinput.dat")
################################################################################################
self.mu = 398600.4418 # global mu matlab
self.segment = 10*(3.14/180) # 10 degree gap convert in radians
self.I_sp = chosen_case['Isp'][0] # in sec
self.m0 = chosen_case['m0'][0] # kg
self.F = chosen_case['force'][0]/1000
self.tol_inc = chosen_case['tol_inc'][0]
self.tol_ecc = chosen_case['tol_ecc'][0]
self.tol_a = chosen_case['tol_a'][0]
self.m_dot = -self.F/self.I_sp/self.g0; # in kg/sec
self.shadow_flag = chosen_case['sh_flag'][0]
self.transfer_case = args.case
self.target_inc, self.target_ecc, self.target_a = 0, 0, 42164
self.tol_a_low, self.tol_a_high = self.target_a - self.tol_a, self.target_a + self.tol_a
w1 = { "a": chosen_case['w1_a'][0], "e": chosen_case['w1_e'][0], "i": chosen_case['w1_i'][0] }
w1_ = { "a_": chosen_case['w1_a_'][0], "e_": chosen_case['w1_e_'][0], "i_": chosen_case['w1_i_'][0] }
c1 = { "a": chosen_case['c1_a'][0], "e": chosen_case['c1_e'][0], "i": chosen_case['c1_i'][0] }
self.weights = {
"w1": w1,
"w1_": w1_,
"c1": c1,
"tau": chosen_case['tau'][0]
}
self.segment_flag = 0
self.time_before_seg_change = []
self.time_after_seg_change = 0
self.time_before_seg_change_1 = 0
def is_terminal(self,state_5):
h, hx, hy, ex, ey= state_5
mu = self.mu # global mu matlab
ecc = math.sqrt((ex**2)+(ey**2))
flag_ecc = 1 if ecc < (self.tol_ecc) else 0
p = (h*h)/mu
a = p/(1-ecc**2)
flag_a = 1 if (self.tol_a_low) < a and a < (self.tol_a_high) else 0
i = ((math.asin(math.sqrt((hx**2)+(hy**2))/h)) / np.pi)*180
flag_inc = 1 if i<(self.tol_inc) else 0
flag = 1 if flag_inc and flag_ecc and flag_a else 0
p_init = ((self.initial_state[0]**2))/mu;
tol_error_a = self.tol_a_high + (self.tol_a_high* (5/100)) # 5 percent of final target a (SHOULD BE 2 PERCENT)
initial_state_ecc = math.sqrt((self.initial_state[3]**2)+(self.initial_state[4]**2))
a_init = p_init/(1-initial_state_ecc**2);
if int(self.transfer_case) > 6:
flag_for_error_a = 1 if (a < 35000) or (a > 52500) else 0 ## only for super GTO case
else:
flag_for_error_a = 1 if (a > tol_error_a) or (a<22000) else 0 ## only for all other GTO cases
red_flag = 1 if flag_for_error_a else 0
monitor_a_flag = 1 if (a < a_init) or (a > self.tol_a_high) else 0
return flag, ecc, self.tol_ecc, a, self.tol_a_low, self.tol_a_high, i,self.tol_inc , 0, flag_for_error_a, 0,monitor_a_flag, red_flag
def get_seg_value(self, state_5):
state_5_1 = state_5[0]
h, hx, hy, ex, ey, _ , _, _= state_5_1
mu = self.mu # global mu matlab
tol_inc = 0.1; # tolerance of inclination +- deg 0.1 0.01
tol_ecc = 0.01; # 0.00001 tolerance of eccentricity +0 0.01 0.00001
tol_a = 42164 * (5/100) # 5,2 perent
# For First segment change
ecc = math.sqrt((ex**2)+(ey**2));
flag_ecc_seg = 1 if ecc < (tol_ecc) else 0
p = (h*h)/mu;
a = p/(1-ecc**2);
flag_a_seg = 1 if (self.target_a - tol_a) < a and a < (self.target_a + tol_a) else 0
i = ((math.asin(math.sqrt((hx**2)+(hy**2))/h)) / np.pi)*180;
flag_inc_seg = 1 if i< (tol_inc) else 0
self.segment = 1*(3.14/180) if flag_inc_seg and flag_ecc_seg and flag_a_seg else 10*(3.14/180)
# For second segment change
tol_inc = 0.1; # tolerance of inclination +- deg 0.1 0.01
tol_ecc = 0.01; # 0.00001 tolerance of eccentricity +0 0.01 0.00001
tol_a_2 = 200 # 300, 30
ecc = math.sqrt((ex**2)+(ey**2));
flag_ecc_seg_2 = 1 if ecc < (tol_ecc) else 0
p = (h*h)/mu;
a = p/(1-ecc**2);
flag_a_seg_2 = 1 if (self.target_a - tol_a) < a and a < (self.target_a + tol_a) else 0
i = ((math.asin(math.sqrt((hx**2)+(hy**2))/h)) / np.pi)*180;
flag_inc_seg_2 = 1 if i< (tol_inc) else 0
if self.transfer_case in [2, 4, 6, 8]: # for 2nd DRL case always it is 0.1 degree
self.segment = 0.1*(3.14/180)
else:
if flag_inc_seg_2 and flag_ecc_seg_2 and flag_a_seg_2:
self.segment = 0.1*(3.14/180)
elif flag_inc_seg and flag_ecc_seg and flag_a_seg:
self.segment = 1*(3.14/180)
else:
self.segment = 10*(3.14/180)
return self.segment
def reset_csv(self):
state = self.initial_state
self.temp = state
self.temp = np.append(self.temp, 0.5)
self.temp = np.append(self.temp, 0.5)
self.temp = np.append(self.temp, self.F)
self.temp = np.append(self.temp, self.segment)
self.temp = np.append(self.temp, self.m0)
self.temp = np.append(self.temp, self.I_sp)
with open(self.completeName_csvlistinitialize , 'w') as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerow(self.temp)
csvfile.close()
#print(a)
return state
def step( self, state, alpha, beta, F, segment, m01, I_sp, Max_R_time , phi_normalizing_factor, timestep):
self.temp = state
segment = self.get_seg_value(state)
if args.case in ['2','4','6','8']:
segment = 0.1*(3.14/180)
# for checking eclipse
eclipse_flag = self.eng.Mat_env.chkEclipse()
if eclipse_flag * self.shadow_flag:
F = 0
self.temp = np.append(self.temp, alpha)
self.temp = np.append(self.temp, beta)
self.temp = np.append(self.temp, F)
self.temp = np.append(self.temp, segment)
self.temp = np.append(self.temp, m01)
self.temp = np.append(self.temp, I_sp)
with open(self.completeName_csvlist, 'w') as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerow(self.temp)
csvfile.close()
a1 = self.eng.Mat_env.resulting()
#print("136env: state", a1)
a3 = list(a1._data)
t_state=a3[0:8] # getting next state
PropellentBurnt = t_state[7]
time_in_days = t_state[6] / (60*60*24)
if (round(t_state[5], 2) % (round((2*np.pi), 2)) ) == 0:
t_state[5] = 0
## Calculating Reward Function (passing ND values)
new_state = [t_state[0], t_state[1], t_state[2], t_state[3], t_state[4]]
ND_state = self.DimtoNonDim_states (t_state , Max_R_time, phi_normalizing_factor)
new_state_ND = [ND_state[0], ND_state[1], ND_state[2], ND_state[3], ND_state[4]]
prev_state = [state[0][0], state[0][1], state[0][2], state[0][3], state[0][4]]
ND_Previous_state = self.DimtoNonDim_states (state[0] , Max_R_time, phi_normalizing_factor)
prev_state_ND = [ND_Previous_state[0], ND_Previous_state[1], ND_Previous_state[2], ND_Previous_state[3], ND_Previous_state[4]]
done,ecc, tol_ecc, a, tol_a_low, tol_a_high, i,tol_inc, flag_ecc, flag_a, flag_inc, monitor_a_flag,redflag =self.is_terminal(t_state[0:5])
target_state_parameters = [ecc,a, i , tol_ecc, tol_a_low, tol_a_high, tol_inc,flag_ecc, flag_a, flag_inc ]
reward, distance, dist_aei, ecc_new, a_new, i_new, ecc_target, a_target, i_target = Enviornment.Reward(self, prev_state_ND,new_state_ND, prev_state,new_state, done,redflag,monitor_a_flag, timestep) #ex,ey,h,Remaining_time
return t_state , reward, distance, dist_aei, 1==done , 1, target_state_parameters, redflag, time_in_days , ecc_new, a_new, i_new, eclipse_flag , self.segment_flag, self.time_before_seg_change_1, self.time_after_seg_change, segment
def DimtoNonDim_states (self, state , Max_R_time, phi_normalizing_factor):
h_1, hx_1, hy_1, ex_1, ey_1, phi_1, time_1, fuel_1 = state
h_1 = h_1 / self.HU # h
hx_1 = hx_1 / self.HU # hx
hy_1 = hy_1 / self.HU # hy
phi_1 = phi_1 / phi_normalizing_factor
time_1 = time_1 / Max_R_time # accumulating time
fuel_1 = fuel_1 / self.m0 # fuel burnt
return [h_1, hx_1, hy_1, ex_1, ey_1, phi_1, time_1, fuel_1]
def NonDimtoDim_states (self, state , Max_R_time, phi_normalizing_factor):
h_1, hx_1, hy_1, ex_1, ey_1, phi_1, time_1, fuel_1 = state
h_1 = h_1 * self.HU # h
hx_1 = hx_1 * self.HU # hx
hy_1 = hy_1 * self.HU # hy
phi_1 = phi_1 * phi_normalizing_factor
time_1 = time_1 * Max_R_time # accumulating time
fuel_1 = fuel_1 * self.m0 # fuel burnt
return [h_1, hx_1, hy_1, ex_1, ey_1, phi_1, time_1, fuel_1]
def Reward (self, prev_state_ND,new_state_ND, prev_state,new_state, done,redflag,monitor_a_flag, timestep):
w1_aei = [self.weights["w1"]["a"] , self.weights["w1"]["e"], self.weights["w1"]["i"]]
w1_aei_ = [self.weights["w1_"]["a_"] , self.weights["w1_"]["e_"], self.weights["w1_"]["i_"] ]
c1_aei = [self.weights["c1"]["a"] , self.weights["c1"]["e"], self.weights["c1"]["i"]]
tauu_aei= self.weights["tau"]
starget = [ [1*(129640.2292/ self.HU)] ,[0] , [0], [0], [0] ]
ecc_prev = math.sqrt((prev_state_ND[3])**2 + (prev_state_ND[4])**2)
ecc_new = math.sqrt((new_state_ND[3])**2 + (new_state_ND[4])**2)
ecc_target = math.sqrt((starget[3][0])**2 + (starget[4][0])**2)
a_prev = (((prev_state_ND[0])**2) /1) / ( 1- (ecc_prev **2))
a_new = (((new_state_ND[0])**2) /1) / ( 1- (ecc_new **2))
a_target = (((starget[0][0])**2) /1) / ( 1- (ecc_target **2))
i_prev = ((math.asin (math.sqrt((prev_state_ND[1]**2)+(prev_state_ND[2]**2))/prev_state_ND[0])) / np.pi)*180
i_new = ((math.asin (math.sqrt((new_state_ND[1]**2)+(new_state_ND[2]**2))/new_state_ND[0])) / np.pi)*180
i_target = ((math.asin (math.sqrt((starget[1][0]**2)+(starget[2][0]**2))/starget[0][0])) / np.pi)*180
i_prev = i_prev /10
i_new = i_new /10
i_target = i_target /10
st_a_e_i_prev = [[a_prev], [ecc_prev], [i_prev]]
st_a_e_i_new = [[a_new], [ecc_new], [i_new]]
st_a_e_i_target = [[a_target], [ecc_target], [i_target]]
exp_value_t_aei = 0
exp_value_t_plus_1_aei = 0
for i in range(0,3):
exp_value_t_aei = exp_value_t_aei + ( c1_aei[i] * math.exp(-(w1_aei_[i] * abs(np.subtract(st_a_e_i_prev[i], st_a_e_i_target[i])) ) ) )
exp_value_t_plus_1_aei = exp_value_t_plus_1_aei + ( c1_aei[i] * math.exp(-(w1_aei_[i] * abs(np.subtract(st_a_e_i_new[i], st_a_e_i_target[i])) ) ) )
phi_st_aei = - np.dot( np.transpose(np.array(w1_aei)) , abs(np.subtract(st_a_e_i_prev ,st_a_e_i_target))) + exp_value_t_aei -(0.03*monitor_a_flag)
phi_st_plus_1_aei = - np.dot( np.transpose(np.array(w1_aei)) , abs(np.subtract(st_a_e_i_new ,st_a_e_i_target))) + exp_value_t_plus_1_aei -(0.03*monitor_a_flag)
reward_t_aei = phi_st_plus_1_aei - phi_st_aei - tauu_aei + (100 * done) - (5*redflag) -(0.00*monitor_a_flag)
diff_a = math.sqrt(( st_a_e_i_target[0][0] - st_a_e_i_new[0][0])**2)
diff_b = math.sqrt((st_a_e_i_target[1][0] - st_a_e_i_new[1][0])**2)
diff_c = math.sqrt((st_a_e_i_target[2][0] - st_a_e_i_new[2][0])**2)
dist_aei = diff_a + diff_b + diff_c
return [reward_t_aei[0]], 0, dist_aei, ecc_new, a_new, i_new, ecc_target, a_target, i_target
def writing_Successful_episodes( self,success_ep_counter, episode, len_episode, score, time,a_last, Inc_last, ecc_last, h_last, hx_last,hy_last,ex_last,ey_last, completeName_successful ):
self.temp = ['Succ_ep_counter : ', success_ep_counter, ' ', ' ',
'ep : ', episode, ' ', ' ', 'ep_length : ', len_episode, ' ', ' ',
'score: ', score, ' ', ' ', 'time : ', time, ' ', ' ',
'targ-a[-1]: ', a_last, ' ', ' ',
'inc[-1]: ', Inc_last, ' ', ' ', 'ecc[-1]: ', ecc_last, ' ', ' ',
'h[-1]: ', h_last, ' ', ' ', 'hx[-1]: ', hx_last, ' ', ' ',
'hy[-1]: ', hy_last, ' ', ' ', 'ex[-1]: ', ex_last, ' ', ' ',
'ey[-1]: ', ey_last]
with open(completeName_successful , 'a') as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerow(self.temp)
csvfile.close()
#print(a)
def writing_all_episodes_data( self, episode, ep_fixed_len_counter, h,hx,hy,ex,ey,phi,ND_h,ND_hx,ND_hy,ND_ex,ND_ey,ND_phi,ecc,a,inc, alpha,beta,thrust, reward_step, score,mass, flag_ecc,flag_a,flag_inc,segment,completeName_successful ):
self.temp = ['ep : ', episode, ' ', 'ep_step : ', ep_fixed_len_counter, ' ', 'state : ', h, hx, hy, ex, ey, phi, ' ', 'ecc,a,inc:', ecc, a, inc, ' ', 'Reward,score : ', reward_step, score, ' ', ' ',
'Normalized_State: ', ND_h, ND_hx, ND_hy, ND_ex, ND_ey, ND_phi, ' ', ' ', 'action_values : ', alpha, beta,thrust, ' ', ' ', 'Mass : ', mass, ' ', ' ', 'flag_ecc : ', flag_ecc, ' ',
'flag_a : ', flag_a, ' ', 'flag_inc : ', flag_inc, ' ', 'Segment : ', segment]
with open(completeName_successful , 'a') as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerow(self.temp)
csvfile.close()
#print(a)
def writing_final_states ( self, h,hx,hy,ex,ey,phi,time,mass, completeName_1 ):
self.temp = []
self.temp = np.append(self.temp, h)
self.temp = np.append(self.temp, hx )
self.temp = np.append(self.temp, hy)
self.temp = np.append(self.temp, ex)
self.temp = np.append(self.temp, ey) # [h;hx;hy;ex;ey;phi;time;fuel_burnt]
self.temp = np.append(self.temp, phi)
self.temp = np.append(self.temp, time)
self.temp = np.append(self.temp, mass)
with open(completeName_1 , 'a') as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerow(self.temp)
csvfile.close()
def plot_variable(env, name, hist, folder_path, ep_counter, flag_ter_values= None, tsp=None, tsp_indexes=None, all_episode_plot_flag=None, flag_saving_with_no_ep_nu =None):
plt.figure(figsize=(12, 8))
if all_episode_plot_flag == 1:
plt.plot(hist[0:-2])
else:
plt.plot(hist[ep_counter-1])
plt.title(f'state parameters values {name}')
plt.ylabel(f'{name.lower()} values')
if flag_ter_values == 1:
plt.axhline(tsp[tsp_indexes[0]], color='r', linestyle='-')
plt.axhline(tsp[tsp_indexes[1]], color='r', linestyle='-')
elif flag_ter_values == 2:
plt.axhline(tsp[tsp_indexes[0]], color='r', linestyle='-')
plt.grid(True)
if flag_saving_with_no_ep_nu == 1:
plt.savefig(folder_path+"/_"+name + ".png")
else:
plt.savefig(folder_path+"_ep_"+str(ep_counter-1) + ".png")
plt.close()
def plot_two_variable(env, name, a,b, hist_1, hist_2, folder_path, ep_counter, flag_ter_values= None, tsp=None, tsp_indexes=None , flag_saving_with_no_ep_nu =None ):
plt.figure(figsize=(12, 8))
plt.plot(hist_1[ep_counter-1], c='b', label=a, linewidth=1.5)
plt.plot(hist_2[ep_counter-1], c='r', label=b, linewidth=1.5)
plt.legend()
plt.grid(True)
plt.title(f'state parameters values {name}')
plt.ylabel(f'{name.lower()} values')
if flag_ter_values == 1:
plt.axhline(tsp[tsp_indexes[0]], color='r', linestyle='-')
plt.axhline(tsp[tsp_indexes[1]], color='r', linestyle='-')
if flag_saving_with_no_ep_nu == 1:
plt.savefig(folder_path+"/_"+name + ".png")
else:
plt.savefig(folder_path+"_ep_"+str(ep_counter-1) + ".png")
plt.close()