├── Code ├── pdfs │ ├── Var-Normal-paths.pdf │ ├── Fig3-Exp2-Dynamic.pdf │ ├── Var-Uniform-paths.pdf │ ├── Fig2-Exp1-NoVariance.pdf │ ├── Fig4-Exp3a-Var-Normal.pdf │ ├── Fig6-Exp3c-Uniform-WC.pdf │ └── Fig5-Exp3b-Var-Uniform.pdf ├── Src │ ├── __pycache__ │ │ ├── environ.cpython-36.pyc │ │ ├── mc_prediction.cpython-36.pyc │ │ └── network_generator.cpython-36.pyc │ ├── environ.py │ ├── e-greedy-presentation.py │ ├── meandelay-uniform-worstcase.py │ ├── meandelay-uniform.py │ ├── meandelay-normal.py │ ├── network_generator.py │ ├── mc_prediction.py │ ├── dynamic-presentation.py │ └── multi_nodes.py ├── Scripts │ ├── Dynamic.sh │ ├── Uniform-WC.sh │ ├── NoVariance.sh │ ├── Var-Normal.sh │ ├── Var-Uniform.sh │ └── Process-res.py ├── runall.sh ├── Plots │ ├── Fig2-Exp1-NoVariance.tex │ ├── Fig3-Exp2-Dynamic.tex │ └── Fig6-Exp3c-Uniform-WC.tex └── Results │ ├── Dynamic │ ├── 20 │ │ └── best_path_proc.csv │ ├── 25 │ │ └── best_path_proc.csv │ ├── 30 │ │ └── best_path_proc.csv │ ├── 35 │ │ └── best_path_proc.csv │ └── 40 │ │ └── best_path_proc.csv │ └── novar │ ├── 20 │ ├── best_path_proc.csv │ └── chosen_path_proc.csv │ ├── 25 │ ├── best_path_proc.csv │ └── chosen_path_proc.csv │ ├── 30 │ ├── best_path_proc.csv │ └── chosen_path_proc.csv │ └── 35 │ └── best_path_proc.csv └── README.md /Code/pdfs/Var-Normal-paths.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AdaptiveRouting-using-RL/AdaptiveRoutingUsingRL/HEAD/Code/pdfs/Var-Normal-paths.pdf -------------------------------------------------------------------------------- /Code/pdfs/Fig3-Exp2-Dynamic.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/AdaptiveRouting-using-RL/AdaptiveRoutingUsingRL/HEAD/Code/pdfs/Fig3-Exp2-Dynamic.pdf -------------------------------------------------------------------------------- /Code/pdfs/Var-Uniform-paths.pdf: 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Src/dynamic-presentation.py 12 | done -------------------------------------------------------------------------------- /Code/Scripts/Uniform-WC.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | for deadline in "${Final_deadline[@]}" 4 | do 5 | mkdir -p Results/uniform-wc/$deadline/ 6 | resultsfile="Results/uniform-wc/$deadline/" 7 | export deadline 8 | export resultsfile 9 | python3 Src/meandelay-uniform-worstcase.py 10 | done -------------------------------------------------------------------------------- /Code/Scripts/NoVariance.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | mkdir -p Results/novar 4 | 5 | for deadline in "${Final_deadline[@]}" 6 | do 7 | mkdir -p Results/novar/$deadline/ 8 | resultsfile="Results/novar/$deadline/" 9 | export deadline 10 | export resultsfile 11 | python3 Src/e-greedy-presentation.py 12 | done 13 | -------------------------------------------------------------------------------- /Code/Scripts/Var-Normal.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | for deadline in "${Final_deadline[@]}" 4 | do 5 | for var in "${variance[@]}" 6 | do 7 | mkdir -p Results/Var/normal/$deadline/$var 8 | resultsfile="Results/Var/normal/$deadline/$var/" 9 | 10 | export resultsfile 11 | export deadline 12 | export var 13 | 14 | python3 Src/meandelay-normal.py 15 | done 16 | done -------------------------------------------------------------------------------- /Code/Scripts/Var-Uniform.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | for deadline in "${Final_deadline[@]}" 4 | do 5 | for var in "${variance[@]}" 6 | do 7 | mkdir -p Results/Var/uniform/$deadline/$var 8 | resultsfile="Results/Var/uniform/$deadline/$var/" 9 | 10 | export resultsfile 11 | export deadline 12 | export var 13 | 14 | python3 Src/meandelay-uniform.py 15 | done 16 | done -------------------------------------------------------------------------------- /Code/runall.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | 3 | Final_deadline=(20 25 30 35 40) 4 | variance=(1 2 3 4 5) 5 | num_episodes=1000 6 | 7 | plot_deadline=20,25,30,35,40 8 | plot_variance=1,2,3,4,5 9 | 10 | export num_episodes 11 | export Final_deadline 12 | export variance 13 | export plot_deadline 14 | export plot_variance 15 | 16 | rm -rf Results/* 17 | 18 | . Scripts/NoVariance.sh 19 | . Scripts/Dynamic.sh 20 | . Scripts/Uniform-WC.sh 21 | . Scripts/Var-Normal.sh 22 | . Scripts/Var-Uniform.sh 23 | 24 | mkdir pdfs 25 | pdflatex -output-directory pdfs Plots/Fig2-Exp1-NoVariance.tex 26 | pdflatex -output-directory pdfs Plots/Fig3-Exp2-Dynamic.tex 27 | pdflatex -output-directory pdfs Plots/Fig4-Exp3a-Var-Normal.tex 28 | pdflatex -output-directory pdfs Plots/Fig5-Exp3b-Var-Uniform.tex 29 | pdflatex -output-directory pdfs Plots/Fig6-Exp3c-Uniform-WC.tex 30 | 31 | #python3 Scripts/Process-res.py 32 | #pdflatex -output-directory pdfs Plots/Var-Normal-paths.tex 33 | #pdflatex -output-directory pdfs Plots/Var-Uniform-paths.tex 34 | 35 | ## Clean up 36 | rm -r pdfs/*.aux 37 | rm -r pdfs/*.log 38 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## RTSS 2019 submission titled "Adaptive Routing with Guaranteed Delay Bounds using Safe Reinforcement Learning" 2 | 3 | 4 | ### Pre-requisities 5 | - Python version 3.7.0 6 | 7 | #### Required python packages 8 | ##### Gym 9 | Gym is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Pinball. Our project uses gym for optimal memory usage and and state space construction. 10 | 11 | Install using the command 12 | `pip3 install gym` 13 | 14 | ##### NetworkX 15 | NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Our project uses NetworkX to generate and manipulate networks. 16 | 17 | Install using the command 18 | `pip3 install networkx` 19 | 20 | ### Instructions 21 | - Make sure the prerequisite packages mentioned above are installed correctly. 22 | - Run script **runall.sh** in the folder Code to run all the experiments described in the paper. 23 | 24 | ### Code folder structure 25 | - **Src.** Contains all the python files 26 | - **Scripts.** Bash scripts for running python files 27 | - **Results.** Generated CSV results are stored here 28 | - **Plots.** Latex files for generating Plots 29 | - **pdfs.** Generated plots are stored here -------------------------------------------------------------------------------- /Code/Src/environ.py: -------------------------------------------------------------------------------- 1 | import gym 2 | from gym import spaces 3 | import networkx as nx 4 | 5 | #Final_deadline = 20 6 | 7 | class RountingEnv(): 8 | """ 9 | Action space is the possible destinations from the node. 10 | Observation is the current node name, amount of time saved, deadline 11 | """ 12 | def __init__(self, G, Final_deadline): 13 | self.action_space = spaces.Discrete(G.number_of_nodes()) # Action space is edges available while sticking to the deadline 14 | self.current_node = G.node[0] 15 | self.next_node = [] 16 | self.total_time = 0 17 | self.Final_deadline = Final_deadline 18 | self.deadline = 0 19 | self.G = G 20 | self._reset(Final_deadline) 21 | 22 | def reset(self, Final_deadline): 23 | return self._reset(Final_deadline) 24 | 25 | def step(self, action, time_traversed): 26 | return self._step(action, time_traversed) 27 | 28 | def _step(self, action, time_traversed): 29 | """ 30 | Main fucntion that does the step action. 31 | Returns: Action: This was the node that was decided to be taken during the sampling 32 | : Reward: The amount of time saved while traversing 33 | : done: This is true if the action/next_node is equal to the destination node. Remember that this is the last entry in the table of nodes 34 | """ 35 | done = False 36 | if(action == int(self.G.nodes[self.G.number_of_nodes() - 1]['index'])): 37 | done = True 38 | next_node = self._get_obs() # Stay in the same node as it is the final node 39 | self.total_time += time_traversed 40 | reward = self.Final_deadline - self.total_time # Reward assignment as the episode is completed 41 | else: # the action taken becomes the next node 42 | next_node = action 43 | reward = 0 44 | self.deadline = self.deadline - time_traversed # new deadline is old deadline - time_traversed. This is the same as reward for now 45 | self.total_time += time_traversed 46 | self.current_node = action 47 | return action, reward, done, {} 48 | 49 | def get_deadline(self): # returns the current deadline 50 | return (self.deadline) 51 | 52 | def _get_obs(self): 53 | return(int(self.G.nodes[self.current_node]['index'])) 54 | 55 | def _reset(self,Final_deadline):# Should reset the environment. Builds the table for all nodes. 56 | self.current_node = 0 57 | self.total_time = 0 58 | self.deadline = Final_deadline # set deadline to final deadline 59 | return self._get_obs() 60 | -------------------------------------------------------------------------------- /Code/Src/e-greedy-presentation.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import random 3 | import os 4 | 5 | from environ import RountingEnv # The routing environment 6 | from collections import defaultdict # memory efficient storing of value functions 7 | from network_generator import prsnt 8 | from mc_prediction import mc_prediction 9 | 10 | import csv 11 | 12 | def make_epsilon_greedy_policy(Q, G, epsilon, observation, deadline): 13 | """ 14 | Creates an epsilon-greedy policy based on a given Q-function and epsilon. 15 | Args: 16 | Q: A dictionary that maps from state -> action-values. 17 | Each value is a numpy array of length nA (see below) 18 | epsilon: The probability to select a random action . float between 0 and 1. 19 | nA: Number of actions in the environment. 20 | Returns: 21 | A function that takes the observation as an argument and returns 22 | the probabilities for each action in the form of a numpy array of length nA. 23 | """ 24 | current_node = observation 25 | time_traversed = 0 26 | next_node = 0 27 | feasible_edges = [] 28 | for i in G[int(current_node)]: 29 | if G[int(current_node)][i]['wct'] <= deadline: 30 | feasible_edges.append(i) 31 | if(feasible_edges == []): # Are there no feasible edges 32 | return(0,0) 33 | if(current_node == G.nodes[G.number_of_nodes() - 1]['index']): # Exit if its the last node already 34 | return(0,0) 35 | probs = np.ones(len(feasible_edges), dtype = float) * epsilon / len(feasible_edges) # probabilities assignment 36 | probs = np.zeros(G.number_of_nodes()) 37 | 38 | unique_nodes = [] 39 | new_probs = epsilon / len(feasible_edges) # Divide by number of edges 40 | 41 | for i in feasible_edges: 42 | probs[feasible_edges] = new_probs 43 | if(all(np.multiply(Q[current_node],probs) == 0.0)): ## If we have no data on the value function, use a feasible edge 44 | best_action = np.argmax(probs) 45 | else: 46 | best_action = np.argmax(np.multiply(Q[current_node],probs)) # Non feasible paths become 0 47 | 48 | probs[best_action] += (1.0 - epsilon) # Add (1-epsilon) to the best action prob 49 | next_edge = np.random.choice(np.arange(len(probs)), p=probs) 50 | time_traversed = G[int(current_node)][int(next_edge)]["tx"] 51 | next_node = next_edge 52 | 53 | return(next_node, time_traversed) 54 | 55 | 56 | deadline = int(os.environ['deadline']) 57 | num_episodes = int(os.environ['num_episodes']) 58 | 59 | print("No Variance Network with deadline ",deadline," with epsiodes", num_episodes) 60 | G = prsnt() 61 | best_path = [G.node[0]['name']] 62 | env = RountingEnv(G, deadline) 63 | Q, policy = mc_prediction(make_epsilon_greedy_policy, env, G, deadline, num_episodes) 64 | -------------------------------------------------------------------------------- /Code/Src/meandelay-uniform-worstcase.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import random 3 | import os 4 | 5 | from environ import RountingEnv # The routing environment 6 | from collections import defaultdict # memory efficient storing of value functions 7 | from network_generator import prsnt 8 | from mc_prediction import mc_prediction 9 | 10 | import csv 11 | 12 | def make_epsilon_greedy_policy(Q, G, epsilon, observation, deadline): 13 | """ 14 | Creates an epsilon-greedy policy based on a given Q-function and epsilon. 15 | Args: 16 | Q: A dictionary that maps from state -> action-values. 17 | Each value is a numpy array of length nA (see below) 18 | epsilon: The probability to select a random action . float between 0 and 1. 19 | nA: Number of actions in the environment. 20 | Returns: 21 | A function that takes the observation as an argument and returns 22 | the probabilities for each action in the form of a numpy array of length nA. 23 | """ 24 | current_node = observation 25 | time_traversed = 0 26 | next_node = 0 27 | feasible_edges = [] 28 | for i in G[int(current_node)]: 29 | if G[int(current_node)][i]['wct'] <= deadline: 30 | feasible_edges.append(i) 31 | if(feasible_edges == []): # Are there no feasible edges 32 | return(0,0) 33 | if(current_node == G.nodes[G.number_of_nodes() - 1]['index']): # Exit if its the last node already 34 | return(0,0) 35 | probs = np.ones(len(feasible_edges), dtype = float) * epsilon / len(feasible_edges) # probabilities assignment 36 | probs = np.zeros(G.number_of_nodes()) 37 | 38 | unique_nodes = [] 39 | new_probs = epsilon / len(feasible_edges) # Divide by number of edges 40 | 41 | for i in feasible_edges: 42 | probs[feasible_edges] = new_probs 43 | if(all(np.multiply(Q[current_node],probs) == 0.0)): ## If we have no data on the value function, use a feasible edge 44 | best_action = np.argmax(probs) 45 | else: 46 | best_action = np.argmax(np.multiply(Q[current_node],probs)) # Non feasible paths become 0 47 | 48 | probs[best_action] += (1.0 - epsilon) # Add (1-epsilon) to the best action prob 49 | next_edge = np.random.choice(np.arange(len(probs)), p=probs) 50 | time_traversed = int(np.random.uniform(0, G[int(current_node)][int(next_edge)]["wc"],1)) 51 | time_traversed = max(time_traversed, 0) 52 | time_traversed = min(time_traversed, G[int(current_node)][int(next_edge)]["wc"]) 53 | next_node = next_edge 54 | 55 | return(next_node, time_traversed) 56 | 57 | deadline = int(os.environ['deadline']) 58 | num_episodes = int(os.environ['num_episodes']) 59 | 60 | print("Network with Uniform distribution worst case. deadline ",deadline," with epsiodes", num_episodes) 61 | 62 | G = prsnt() 63 | best_path = [G.node[0]['name']] 64 | env = RountingEnv(G, deadline) 65 | Q, policy = mc_prediction(make_epsilon_greedy_policy, env, G, deadline, num_episodes) 66 | -------------------------------------------------------------------------------- /Code/Src/meandelay-uniform.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import random 3 | import os 4 | 5 | from environ import RountingEnv # The routing environment 6 | from collections import defaultdict # memory efficient storing of value functions 7 | from network_generator import prsnt 8 | from mc_prediction import mc_prediction 9 | 10 | import csv 11 | 12 | def make_epsilon_greedy_policy(Q, G, epsilon, observation, deadline): 13 | """ 14 | Creates an epsilon-greedy policy based on a given Q-function and epsilon. 15 | Args: 16 | Q: A dictionary that maps from state -> action-values. 17 | Each value is a numpy array of length nA (see below) 18 | epsilon: The probability to select a random action . float between 0 and 1. 19 | nA: Number of actions in the environment. 20 | Returns: 21 | A function that takes the observation as an argument and returns 22 | the probabilities for each action in the form of a numpy array of length nA. 23 | """ 24 | current_node = observation 25 | time_traversed = 0 26 | next_node = 0 27 | feasible_edges = [] 28 | for i in G[int(current_node)]: 29 | if G[int(current_node)][i]['wct'] <= deadline: 30 | feasible_edges.append(i) 31 | if(feasible_edges == []): # Are there no feasible edges 32 | return(0,0) 33 | 34 | if(current_node == G.nodes[G.number_of_nodes() - 1]['index']): # Exit if its the last node already 35 | return(0,0) 36 | 37 | probs = np.ones(len(feasible_edges), dtype = float) * epsilon / len(feasible_edges) # probabilities assignment 38 | probs = np.zeros(G.number_of_nodes()) 39 | 40 | unique_nodes = [] 41 | new_probs = epsilon / len(feasible_edges) # Divide by number of edges 42 | 43 | for i in feasible_edges: 44 | probs[feasible_edges] = new_probs 45 | if(all(np.multiply(Q[current_node],probs) == 0.0)): ## If we have no data on the value function, use a feasible edge 46 | best_action = np.argmax(probs) 47 | else: 48 | best_action = np.argmax(np.multiply(Q[current_node],probs)) # Non feasible paths become 0 49 | 50 | probs[best_action] += (1.0 - epsilon) # Add (1-epsilon) to the best action prob 51 | next_edge = np.random.choice(np.arange(len(probs)), p=probs) 52 | variance = int(os.environ['var']) 53 | time_traversed = int(np.random.uniform(G[int(current_node)][int(next_edge)]["tx"] - variance, G[int(current_node)][int(next_edge)]["tx"] +variance,1)) 54 | time_traversed = max(time_traversed, 0) 55 | time_traversed = min(time_traversed, G[int(current_node)][int(next_edge)]["wc"]) 56 | next_node = next_edge 57 | 58 | return(next_node, time_traversed) 59 | 60 | 61 | deadline = int(os.environ['deadline']) 62 | num_episodes = int(os.environ['num_episodes']) 63 | variance = int(os.environ['var']) 64 | 65 | print("Network with Uniform distribution. deadline: ",deadline,", variance: ",variance,", epsiodes: ", num_episodes) 66 | 67 | G = prsnt() 68 | best_path = [G.node[0]['name']] 69 | env = RountingEnv(G, deadline) 70 | Q, policy = mc_prediction(make_epsilon_greedy_policy, env, G, deadline, num_episodes) 71 | -------------------------------------------------------------------------------- /Code/Src/meandelay-normal.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import random 3 | import os 4 | import scipy.stats as stats 5 | 6 | from environ import RountingEnv # The routing environment 7 | from collections import defaultdict # memory efficient storing of value functions 8 | from network_generator import prsnt 9 | from mc_prediction import mc_prediction 10 | 11 | import csv 12 | 13 | def make_epsilon_greedy_policy(Q, G, epsilon, observation, deadline): 14 | """ 15 | Creates an epsilon-greedy policy based on a given Q-function and epsilon. 16 | Args: 17 | Q: A dictionary that maps from state -> action-values. 18 | Each value is a numpy array of length nA (see below) 19 | epsilon: The probability to select a random action . float between 0 and 1. 20 | nA: Number of actions in the environment. 21 | Returns: 22 | A function that takes the observation as an argument and returns 23 | the probabilities for each action in the form of a numpy array of length nA. 24 | """ 25 | current_node = observation 26 | time_traversed = 0 27 | next_node = 0 28 | feasible_edges = [] 29 | for i in G[int(current_node)]: 30 | if G[int(current_node)][i]['wct'] <= deadline: 31 | feasible_edges.append(i) 32 | if(feasible_edges == []): # Are there no feasible edges 33 | print("The Schedule is not feasible") 34 | return(0,0) 35 | if(current_node == G.nodes[G.number_of_nodes() - 1]['index']): # Exit if its the last node already 36 | return(0,0) 37 | probs = np.ones(len(feasible_edges), dtype = float) * epsilon / len(feasible_edges) # probabilities assignment 38 | probs = np.zeros(G.number_of_nodes()) 39 | 40 | unique_nodes = [] 41 | new_probs = epsilon / len(feasible_edges) # Divide by number of edges 42 | for i in feasible_edges: 43 | probs[feasible_edges] = new_probs 44 | if(all(np.multiply(Q[current_node],probs) == 0.0)): ## If we have no data on the value function, use a feasible edge 45 | best_action = np.argmax(probs) 46 | else: 47 | best_action = np.argmax(np.multiply(Q[current_node],probs)) # Non feasible paths become 0 48 | 49 | probs[best_action] += (1.0 - epsilon) # Add (1-epsilon) to the best action prob 50 | next_edge = np.random.choice(np.arange(len(probs)), p=probs) 51 | variance = int(os.environ['var']) 52 | 53 | lower, upper = 0, G[int(current_node)][int(next_edge)]["wc"] # Set the upper and lower limits 54 | mu, sigma = G[int(current_node)][int(next_edge)]["tx"], variance # Mean = mean tx, variance 55 | time_traversed = stats.truncnorm.rvs((lower - mu) / sigma, (upper - mu) / sigma, mu) 56 | 57 | next_node = next_edge 58 | 59 | return(next_node, time_traversed) 60 | 61 | 62 | deadline = int(os.environ['deadline']) 63 | num_episodes = int(os.environ['num_episodes']) 64 | variance = int(os.environ['var']) 65 | 66 | print("Network with Normal distribution. deadline: ",deadline,", variance: ",variance,", epsiodes: ", num_episodes) 67 | G = prsnt() 68 | best_path = [G.node[0]['name']] 69 | env = RountingEnv(G, deadline) 70 | Q, policy = mc_prediction(make_epsilon_greedy_policy, env, G, deadline, num_episodes) 71 | -------------------------------------------------------------------------------- /Code/Plots/Fig2-Exp1-NoVariance.tex: -------------------------------------------------------------------------------- 1 | \documentclass{standalone} 2 | 3 | \usepackage{tikz,pgfplots} 4 | \usepackage{pgfplotstable} 5 | \usepgfplotslibrary{groupplots} 6 | %\pgfplotsset{compat=1.14} 7 | 8 | 9 | \begin{document} 10 | 11 | \pgfplotstableread[col sep = comma]{Results/novar/20/tx_times.csv}\txtwenty % 12 | \pgfplotstableread[col sep = comma]{Results/novar/25/tx_times.csv}\txtwentyfive % 13 | \pgfplotstableread[col sep = comma]{Results/novar/30/tx_times.csv}\txthirty % 14 | \pgfplotstableread[col sep = comma]{Results/novar/35/tx_times.csv}\txthirtyfive % 15 | \pgfplotstableread[col sep = comma]{Results/novar/40/tx_times.csv}\txforty % 16 | 17 | \begin{tikzpicture} 18 | \begin{groupplot}[% 19 | group style = {group size = 1 by 5, 20 | vertical sep = 0.5 cm, 21 | y descriptions at = edge left}, 22 | height=3.2cm, 23 | ymin = 0, 24 | ymax = 41, 25 | xmin = 0, 26 | xmax = 500, 27 | width=\linewidth, 28 | grid = major, 29 | scaled x ticks = false, 30 | scaled y ticks = false, 31 | x tick label style = {/pgf/number format/fixed}, 32 | y tick label style = {/pgf/number format/fixed}, 33 | legend style = {draw=none,font=\scriptsize}, 34 | ] 35 | 36 | \nextgroupplot[% 37 | legend columns=5, 38 | legend style={at={(0.5,1)},anchor=south}, 39 | xticklabels={,,}, 40 | title = {\scriptsize Deadline $D_F$ = 20}, 41 | title style={at={(0.5,0.8)}}, 42 | ] 43 | \addplot[mark=none, blue, dashed, forget plot, thick] coordinates {(0,20) (1000,20)}; 44 | \addplot[blue, only marks, mark=o, each nth point=5] table [x index = {0}, y index = {1}]{\txtwenty}; 45 | 46 | \nextgroupplot[% 47 | legend columns=5, 48 | legend style={at={(0.5,1)},anchor=south}, 49 | xticklabels={,,}, 50 | title = {\scriptsize Deadline $D_F$ = 25}, 51 | title style={at={(0.5,0.8)}}, 52 | ] 53 | \addplot[mark=none, red, dashed, forget plot, thick] coordinates {(0,25) (1000,25)}; 54 | \addplot[red, only marks, mark=o, each nth point=5] table [x index = {0}, y index = {1}]{\txtwentyfive}; 55 | 56 | \nextgroupplot[% 57 | legend columns=5, 58 | legend style={at={(0.5,1)},anchor=south}, 59 | ylabel = {Transmission Time}, 60 | xticklabels={,,}, 61 | title = {\scriptsize Deadline $D_F$ = 30}, 62 | title style={at={(0.5,0.8)}}, 63 | ] 64 | 65 | \addplot[mark=none, green!30!black, dashed, forget plot, thick] coordinates {(0,30) (1000,30)}; 66 | \addplot[ green!30!black, only marks, mark=o, each nth point=5] table [x index = {0}, y index = {1}]{\txthirty}; 67 | 68 | \nextgroupplot[% 69 | legend columns=5, 70 | legend style={at={(0.5,1)},anchor=south}, 71 | xticklabels={,,}, 72 | title = {\scriptsize Deadline $D_F$ = 35}, 73 | title style={at={(0.5,0.8)}}, 74 | ] 75 | 76 | \addplot[mark=none, violet, dashed, forget plot, thick] coordinates {(0,35) (1000,35)}; 77 | \addplot[violet, only marks, mark=o, each nth point=5] table [x index = {0}, y index = {1}]{\txthirtyfive}; 78 | 79 | \nextgroupplot[% 80 | legend columns=5, 81 | legend style={at={(0.5,1)},anchor=south}, 82 | xlabel = {Packet / Episode No.}, 83 | title = {\scriptsize Deadline $D_F$ = 40}, 84 | title style={at={(0.5,0.8)}}, 85 | ] 86 | 87 | \addplot[mark=none, cyan, dashed, forget plot, thick] coordinates {(0,40) (1000,40)}; 88 | \addplot[cyan, only marks, mark=o, each nth point=5] table [x index = {0}, y index = {1}]{\txforty}; 89 | 90 | \end{groupplot} 91 | \end{tikzpicture} 92 | 93 | \end{document} -------------------------------------------------------------------------------- /Code/Plots/Fig3-Exp2-Dynamic.tex: -------------------------------------------------------------------------------- 1 | \documentclass{standalone} 2 | 3 | \usepackage{tikz,pgfplots} 4 | \usepackage{pgfplotstable} 5 | \usepgfplotslibrary{groupplots} 6 | %\pgfplotsset{compat=1.14} 7 | 8 | 9 | \begin{document} 10 | 11 | \pgfplotstableread[col sep = comma]{Results/Dynamic/20/tx_times.csv}\txtwenty % 12 | \pgfplotstableread[col sep = comma]{Results/Dynamic/25/tx_times.csv}\txtwentyfive % 13 | \pgfplotstableread[col sep = comma]{Results/Dynamic/30/tx_times.csv}\txthirty % 14 | \pgfplotstableread[col sep = comma]{Results/Dynamic/35/tx_times.csv}\txthirtyfive % 15 | \pgfplotstableread[col sep = comma]{Results/Dynamic/40/tx_times.csv}\txforty % 16 | 17 | \begin{tikzpicture} 18 | \begin{groupplot}[% 19 | group style = {group size = 1 by 5, 20 | vertical sep = 0.5 cm, 21 | y descriptions at = edge left}, 22 | height=3.2cm, 23 | ymin = 0, 24 | ymax = 41, 25 | xmin = 0, 26 | xmax = 500, 27 | width=\linewidth, 28 | grid = major, 29 | scaled x ticks = false, 30 | scaled y ticks = false, 31 | x tick label style = {/pgf/number format/fixed}, 32 | y tick label style = {/pgf/number format/fixed}, 33 | legend style = {draw=none,font=\scriptsize}, 34 | ] 35 | 36 | \nextgroupplot[% 37 | legend columns=5, 38 | legend style={at={(0.5,1)},anchor=south}, 39 | xticklabels={,,}, 40 | title = {\scriptsize Deadline $D_F$ = 20}, 41 | title style={at={(0.5,0.8)}}, 42 | ] 43 | \addplot[mark=none, blue, dashed, forget plot, thick] coordinates {(0,20) (1000,20)}; 44 | \addplot[blue, only marks, mark=o, each nth point=1] table [x index = {0}, y index = {1}]{\txtwenty}; 45 | 46 | \nextgroupplot[% 47 | legend columns=5, 48 | legend style={at={(0.5,1)},anchor=south}, 49 | xticklabels={,,}, 50 | title = {\scriptsize Deadline $D_F$ = 25}, 51 | title style={at={(0.5,0.8)}}, 52 | ] 53 | \addplot[mark=none, red, dashed, forget plot, thick] coordinates {(0,25) (1000,25)}; 54 | \addplot[red, only marks, mark=o, each nth point=1] table [x index = {0}, y index = {1}]{\txtwentyfive}; 55 | 56 | \nextgroupplot[% 57 | legend columns=5, 58 | legend style={at={(0.5,1)},anchor=south}, 59 | ylabel = {Transmission Time}, 60 | xticklabels={,,}, 61 | title = {\scriptsize Deadline $D_F$ = 30}, 62 | title style={at={(0.5,0.8)}}, 63 | ] 64 | 65 | \addplot[mark=none, green!30!black, dashed, forget plot, thick] coordinates {(0,30) (1000,30)}; 66 | \addplot[ green!30!black, only marks, mark=o, each nth point=1] table [x index = {0}, y index = {1}]{\txthirty}; 67 | 68 | \nextgroupplot[% 69 | legend columns=5, 70 | legend style={at={(0.5,1)},anchor=south}, 71 | xticklabels={,,}, 72 | title = {\scriptsize Deadline $D_F$ = 35}, 73 | title style={at={(0.5,0.8)}}, 74 | ] 75 | 76 | \addplot[mark=none, violet, dashed, forget plot, thick] coordinates {(0,35) (1000,35)}; 77 | \addplot[violet, only marks, mark=o, each nth point=1] table [x index = {0}, y index = {1}]{\txthirtyfive}; 78 | 79 | \nextgroupplot[% 80 | legend columns=5, 81 | legend style={at={(0.5,1)},anchor=south}, 82 | xlabel = {Packet / Episode No.}, 83 | title = {\scriptsize Deadline $D_F$ = 40}, 84 | title style={at={(0.5,0.8)}}, 85 | ] 86 | 87 | \addplot[mark=none, cyan, dashed, forget plot, thick] coordinates {(0,40) (1000,40)}; 88 | \addplot[cyan, only marks, mark=o, each nth point=1] table [x index = {0}, y index = {1}]{\txforty}; 89 | 90 | \end{groupplot} 91 | \end{tikzpicture} 92 | 93 | \end{document} -------------------------------------------------------------------------------- /Code/Src/network_generator.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import networkx as nx 3 | import matplotlib.pyplot as plt 4 | 5 | def paper_network(): 6 | # Create nodes. To keep track of nodes, first one is the 7 | s = Node('s', [[]], ['t', 'v1']) 8 | v1 = Node('v1', [[]], ['s', 'v2', 't']) 9 | v2 = Node('v2', [[]], ['t']) 10 | t = Node('t', [(None,0)], []) 11 | 12 | # Create edges between the nodes 13 | ## Node s 14 | s.create_edge(v1, 4, 10) 15 | s.create_edge(t, 15, 25) 16 | ## Node v1 17 | v1.create_edge(t, 12, 15) 18 | v1.create_edge(v2, 4, 10) 19 | ## Node v2 20 | v2.create_edge(t, 4, 10) 21 | return s 22 | 23 | def prsnt(): 24 | G = nx.DiGraph() 25 | 26 | G.add_node(0, name='i', index='0') 27 | G.add_node(1, name='x', index='1') 28 | G.add_node(2, name='y', index='2') 29 | G.add_node(3, name='z', index='3') 30 | G.add_node(4, name='t', index='4') 31 | 32 | edges = [(0,1),(0,4),(1,2),(1,3),(1,4),(2,4),(3,4)] 33 | 34 | G.add_edge(0,1,tx=4,wc=10) 35 | G.add_edge(0,4,tx=12,wc=25) 36 | G.add_edge(1,2,tx=3,wc=10) 37 | G.add_edge(1,3,tx=1,wc=15) 38 | G.add_edge(1,4,tx=10,wc=10) 39 | G.add_edge(2,4,tx=3,wc=10) 40 | G.add_edge(3,4,tx=1,wc=15) 41 | 42 | for node in G.nodes(): 43 | for edge in G[node]: 44 | G[node][edge]["wct"] = G[node][edge]["wc"] + nx.dijkstra_path_length(G,edge,G.number_of_nodes()-1,weight='wc') 45 | return G 46 | 47 | def random_dag(nodes, edges): 48 | """Generate a random Directed Acyclic Graph (DAG) with a given number of nodes and edges.""" 49 | typ_tx_min = 4 50 | typ_tx_max = 10 51 | 52 | wc_min = 10 53 | wc_max = 30 54 | 55 | num_nodes = nodes 56 | num_edges = edges 57 | 58 | G = nx.DiGraph() 59 | 60 | G.add_node(0, name = 'i', index = '0') ## Create the inital node 61 | G.add_edge(0,1,tx=4,wc=10) # create edge from init to first node 62 | G.add_edge(nodes-2,nodes-1,tx=4,wc=10)# create edge from penultimate node to the final node 63 | G.add_node(nodes-1, name = 't', index = str(nodes-1)) ## Create the destination node 64 | 65 | for i in range(nodes - 1): 66 | G.add_node(i, index=str(i)) 67 | 68 | while edges > 0: 69 | a = np.random.randint(1,nodes-1) 70 | b=a 71 | while b==a: 72 | b = np.random.randint(1,nodes-1) 73 | 74 | typ_tx = int(np.random.uniform(typ_tx_min, typ_tx_max, 1)) 75 | wc_tx = int(np.random.uniform(wc_min, wc_max, 1)) 76 | G.add_edge(a, b, tx=typ_tx, wc=wc_tx) 77 | 78 | if b > a: 79 | edges -= 1 80 | else: 81 | # we closed a loop! 82 | G.remove_edge(a,b) 83 | 84 | for i in range(nodes - 2,1,-1): ## check that there exists a path to dest 85 | if(nx.has_path(G,i,nodes-1) == False): 86 | typ_tx = int(np.random.uniform(typ_tx_min, typ_tx_max, 1)) 87 | wc_tx = int(np.random.uniform(wc_min, wc_max, 1)) 88 | G.add_edge(i, nodes-1, tx=typ_tx, wc=wc_tx)#Create an edge 89 | 90 | while nx.has_path(G,0,nodes-1) != True: #make sure theres a path from source to destination 91 | G = random_dag(num_nodes,num_edges) 92 | 93 | for node in G.nodes(): 94 | for edge in G[node]: 95 | try: 96 | G[node][edge]["wct"] = G[node][edge]["wc"] + nx.dijkstra_path_length(G,edge,G.number_of_nodes()-1,weight='wc') 97 | except nx.NetworkXNoPath: 98 | print("not found") 99 | return G 100 | -------------------------------------------------------------------------------- /Code/Src/mc_prediction.py: -------------------------------------------------------------------------------- 1 | import sys 2 | import os 3 | import numpy as np 4 | import csv 5 | import time 6 | 7 | from environ import RountingEnv # The routing environment 8 | from collections import defaultdict # memory efficient storing of value functions 9 | 10 | def mc_prediction(policy, env,G, Final_deadline, num_episodes, discount_factor=1, alpha=0.9, epsilon=0.1): 11 | """ 12 | Monte Carlo prediction algorithm. Calculates the value function 13 | for a given policy using sampling. 14 | 15 | Args: 16 | policy: A function that maps an observation to action probabilities. 17 | env:Routing environment. 18 | num_episodes: Number of episodes to sample. 19 | discount_factor: Gamma discount factor. 20 | 21 | Returns: 22 | A dictionary that maps from state -> value. 23 | The state is a tuple and the value is a float. 24 | """ 25 | returns_time = defaultdict(float) # Keeps track of time and count of returns for each state 26 | returns_count = defaultdict(float) 27 | Q = defaultdict(lambda: np.zeros(G.number_of_nodes())) # The final value function 28 | eps_time = np.empty(num_episodes+1) 29 | for i_episode in range(1, num_episodes + 1): 30 | start_time = time.time() # Time keeping 31 | if i_episode > 40: # this will increase the time traversed after episode 40 32 | time_offset = 0 33 | episode = []# An episode is an array of (state, action, reward) tuples 34 | total_time = 0 35 | state = env.reset(Final_deadline) 36 | for t in range(100): 37 | deadline = env.get_deadline() 38 | action, time_traversed = policy(Q, G, epsilon, state, deadline) 39 | total_time += time_traversed 40 | if ([action, time_traversed] == [None,0]): 41 | break 42 | 43 | next_state, reward, done, _ = env.step(action, time_traversed) 44 | best_next_action = np.argmax(Q[next_state]) 45 | td_target = reward + discount_factor * Q[next_state][best_next_action] 46 | td_delta = td_target - Q[state][action] 47 | Q[state][action] += alpha * td_delta 48 | 49 | episode.append(action) 50 | 51 | if done: 52 | eps_time[i_episode] = time.time() - start_time 53 | break 54 | state = next_state 55 | 56 | row = [] 57 | filename = os.environ['resultsfile'] ## Write a result file after each episode 58 | 59 | w = csv.writer(open(filename+"Q_values.csv", "a+")) 60 | for key, val in Q.items(): 61 | row = [i_episode, key] 62 | for v in range(len(val)): 63 | row.append(val[v]) 64 | w.writerow(row) 65 | 66 | row = [] 67 | w = csv.writer(open(filename+"best_path.csv", "a+")) 68 | row = [i_episode, G.nodes[0]['name']] 69 | for state,action in Q.items(): 70 | next_node = G.nodes[np.argmax(action)]['index'] 71 | if(next_node != G.nodes[0]['index']): 72 | row.append(G.nodes[int(next_node)]['name']) 73 | if(next_node == G.nodes[G.number_of_nodes() - 1]['index'] ): # If it is the final node 74 | break 75 | w.writerow(row) 76 | 77 | row = [] 78 | w = csv.writer(open(filename+"chosen_path.csv", "a+")) 79 | row = [i_episode, G.nodes[0]['name']] 80 | for node in episode: 81 | row.append(G.nodes[int(node)]['name']) 82 | w.writerow(row) 83 | 84 | row = [] 85 | row = [i_episode, total_time] 86 | w = csv.writer(open(filename+"tx_times.csv", "a+")) 87 | w.writerow(row) 88 | 89 | np.savetxt(filename+"comp_times.csv", eps_time, delimiter=",") 90 | 91 | return Q, policy 92 | -------------------------------------------------------------------------------- /Code/Plots/Fig6-Exp3c-Uniform-WC.tex: -------------------------------------------------------------------------------- 1 | \documentclass{standalone} 2 | 3 | \usepackage{tikz,pgfplots} 4 | \usepackage{pgfplotstable} 5 | \usepgfplotslibrary{groupplots} 6 | %\pgfplotsset{compat=1.14} 7 | 8 | 9 | \begin{document} 10 | 11 | \pgfplotstableread[col sep = comma]{Results/uniform-wc/20/tx_times.csv}\txtwenty % 12 | \pgfplotstableread[col sep = comma]{Results/uniform-wc/25/tx_times.csv}\txtwentyfive % 13 | \pgfplotstableread[col sep = comma]{Results/uniform-wc/30/tx_times.csv}\txthirty % 14 | \pgfplotstableread[col sep = comma]{Results/uniform-wc/35/tx_times.csv}\txthirtyfive % 15 | \pgfplotstableread[col sep = comma]{Results/uniform-wc/40/tx_times.csv}\txforty % 16 | 17 | \begin{tikzpicture} 18 | \begin{groupplot}[% 19 | group style = {group size = 1 by 5, 20 | vertical sep = 0.5 cm, 21 | y descriptions at = edge left}, 22 | height=3cm, 23 | width=\linewidth, 24 | grid = major, 25 | scaled x ticks = false, 26 | scaled y ticks = false, 27 | x tick label style = {/pgf/number format/fixed}, 28 | y tick label style = {/pgf/number format/fixed}, 29 | legend style = {draw=none,font=\scriptsize}, 30 | ] 31 | 32 | \nextgroupplot[% 33 | every mark/.append style={mark size=4pt}, 34 | xmin = 0, 35 | xmax = 1000, 36 | ymin = 0, 37 | ymax = 30, 38 | legend columns=5, 39 | legend style={at={(0.5,1)},anchor=south}, 40 | %xlabel = {Packet / Episode No.}, 41 | xticklabels={,,}, 42 | title = {\scriptsize Deadline $D_F$ = 20}, 43 | title style={at={(0.5,0.8)}}, 44 | %legend entries = {$D$ = 20},%, $D$ = 25,$D$ = 30,$D$ = 35,$D$ = 40,}, 45 | ] 46 | \addplot[mark=none, blue, dashed, forget plot] coordinates {(0,20) (1000,20)}; 47 | % \addplot[mark=none, red, dashed, forget plot] coordinates {(0,25) (1000,25)}; 48 | % \addplot[mark=none, green!30!black, dashed, forget plot] coordinates {(0,30) (1000,30)}; 49 | % \addplot[mark=none, violet, dashed, forget plot] coordinates {(0,35) (1000,35)}; 50 | % \addplot[mark=none, cyan, dashed, forget plot] coordinates {(0,40) (1000,40)}; 51 | 52 | % \addplot[ blue!40!white]table[x index = {0}, y index = {1}]{\txtwenty}; 53 | % \addplot[ red!40!white]table[x index = {0}, y index = {1}]{\txtwentyfive}; 54 | % \addplot[ darkgray!40!white]table[x index = {0}, y index = {1}]{\txthirty}; 55 | % \addplot[ violet!40!white]table[x index = {0}, y index = {1}]{\txthirtyfive}; 56 | % \addplot[ cyan!40!white]table[x index = {0}, y index = {1}]{\txforty}; 57 | 58 | \addplot[ blue, only marks, mark=o, each nth point=5]table[x index = {0}, y index = {1}]{\txtwenty}; 59 | % \addplot[ red,only marks,each nth point=10]table[x index = {0}, y index = {1}]{\txtwentyfive}; 60 | % \addplot[ green!30!black,only marks,each nth point=10]table[x index = {0}, y index = {1}]{\txthirty}; 61 | % \addplot[ violet,only marks,each nth point=10]table[x index = {0}, y index = {1}]{\txthirtyfive}; 62 | % \addplot[ cyan,only marks,each nth point=10]table[x index = {0}, y index = {1}]{\txforty}; 63 | 64 | \nextgroupplot[% 65 | every mark/.append style={mark size=4pt}, 66 | xmin = 0, 67 | xmax = 1000, 68 | ymin = 0, 69 | ymax = 30, 70 | legend columns=5, 71 | legend style={at={(0.5,1)},anchor=south}, 72 | xticklabels={,,}, 73 | title = {\scriptsize Deadline $D_F$ = 25}, 74 | title style={at={(0.5,0.8)}}, 75 | %xlabel = {Packet / Episode No.}, 76 | %legend entries = {$D$ = 20, $D$ = 25,$D$ = 30,$D$ = 35,$D$ = 40, 77 | % }, 78 | ] 79 | \addplot[mark=none, red, dashed, forget plot] coordinates {(0,25) (1000,25)}; 80 | \addplot[ red,only marks, mark=o,each nth point=10]table[x index = {0}, y index = {1}]{\txtwentyfive}; 81 | 82 | \nextgroupplot[% 83 | every mark/.append style={mark size=4pt}, 84 | xmin = 0, 85 | xmax = 1000, 86 | ymin = 0, 87 | ymax = 40, 88 | legend columns=5, 89 | legend style={at={(0.5,1)},anchor=south}, 90 | ylabel = {Transmission Time}, 91 | xticklabels={,,}, 92 | title = {\scriptsize Deadline $D_F$ = 30}, 93 | title style={at={(0.5,0.8)}}, 94 | %xlabel = {Packet / Episode No.}, 95 | %legend entries = {$D$ = 20, $D$ = 25,$D$ = 30,$D$ = 35,$D$ = 40, 96 | % }, 97 | ] 98 | 99 | \addplot[mark=none, green!30!black, dashed, forget plot] coordinates {(0,30) (1000,30)}; 100 | \addplot[ green!30!black,only marks, mark=o,each nth point=10]table[x index = {0}, y index = {1}]{\txthirty}; 101 | 102 | \nextgroupplot[% 103 | every mark/.append style={mark size=4pt}, 104 | xmin = 0, 105 | xmax = 1000, 106 | ymin = 0, 107 | ymax = 40, 108 | legend columns=5, 109 | legend style={at={(0.5,1)},anchor=south}, 110 | xticklabels={,,}, 111 | title = {\scriptsize Deadline $D_F$ = 35}, 112 | title style={at={(0.5,0.8)}}, 113 | %xlabel = {Packet / Episode No.}, 114 | %legend entries = {$D$ = 20, $D$ = 25,$D$ = 30,$D$ = 35,$D$ = 40, 115 | % }, 116 | ] 117 | 118 | \addplot[mark=none, violet, dashed, forget plot] coordinates {(0,35) (1000,35)}; 119 | \addplot[ violet,only marks, mark=o, each nth point=10]table[x index = {0}, y index = {1}]{\txthirtyfive}; 120 | \nextgroupplot[% 121 | every mark/.append style={mark size=4pt}, 122 | xmin = 0, 123 | xmax = 1000, 124 | ymin = 0, 125 | ymax = 50, 126 | legend columns=5, 127 | legend style={at={(0.5,1)},anchor=south}, 128 | xlabel = {Packet / Episode No.}, 129 | title = {\scriptsize Deadline $D_F$ = 40}, 130 | title style={at={(0.5,0.8)}}, 131 | % legend entries = {$D$ = 20, $D$ = 25,$D$ = 30,$D$ = 35,$D$ = 40, 132 | % }, 133 | ] 134 | 135 | \addplot[mark=none, cyan, dashed, forget plot] coordinates {(0,40) (1000,40)}; 136 | \addplot[ cyan,only marks, mark=o,each nth point=10]table[x index = {0}, y index = {1}]{\txforty}; 137 | 138 | %\nextgroupplot[% 139 | % xmin = 0, 140 | % xmax = 0.8 * 10 ^ 5, 141 | % ymin = 0.4, 142 | % ymax = 1, 143 | % xlabel = {Time [s]}, 144 | % scaled x ticks = base 10:-3, 145 | % xtick scale label code/.code={}, 146 | % ylabel = {SSIM}, 147 | % ytick={0.4, 0.6, 0.8, 1.0}, 148 | % yticklabels={0.4, 0.6, 0.8, 1.0}, 149 | % no markers, 150 | % legend entries = {Camera 1, Camera 2}, 151 | % legend style={at={(1,-0.05)}, anchor=south east, fill=none}, 152 | %] 153 | % 154 | % \addplot[ blue] table[mark = none, y index = {1}] 155 | % {\noadaptssimone}; 156 | % \addplot[ red, dashed] table[mark = none, y index = {1}] 157 | % {\noadaptssimtwo}; 158 | 159 | \end{groupplot} 160 | \end{tikzpicture} 161 | 162 | \end{document} -------------------------------------------------------------------------------- /Code/Src/dynamic-presentation.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import random 3 | import os 4 | import sys 5 | import time 6 | 7 | from environ import RountingEnv # The routing environment 8 | from collections import defaultdict # memory efficient storing of value functions 9 | from network_generator import prsnt 10 | 11 | import csv 12 | def mc_prediction(policy, env,G, Final_deadline, num_episodes, discount_factor=0.75, alpha=1, epsilon=0.1): 13 | """ 14 | Monte Carlo prediction algorithm. Calculates the value function 15 | for a given policy using sampling. 16 | 17 | Args: 18 | policy: A function that maps an observation to action probabilities. 19 | env:Routing environment. 20 | num_episodes: Number of episodes to sample. 21 | discount_factor: Gamma discount factor. 22 | 23 | Returns: 24 | A dictionary that maps from state -> value. 25 | The state is a tuple and the value is a float. 26 | """ 27 | start_time = time.time()# Keeps track of time and count of returns for each state 28 | returns_time = defaultdict(float) 29 | returns_count = defaultdict(float) 30 | Q = defaultdict(lambda: np.zeros(G.number_of_nodes()))# The final value function 31 | eps_time = np.empty(num_episodes+1) 32 | for i_episode in range(1, num_episodes + 1): 33 | start_time = time.time() # Time keeping 34 | time_offset = 0 35 | if i_episode > 40: # ADDS A DYNAMIC CHANGE AFTER 40 EPSIDOES 36 | time_offset = 6 37 | 38 | # Generate an episode. 39 | episode = []# An episode is an array of (state, action, reward) tuples 40 | total_time = 0 41 | state = env.reset(Final_deadline) 42 | for t in range(100): 43 | deadline = env.get_deadline() 44 | action, time_traversed = policy(Q, G, np.minimum(1,1 * np.exp(-(i_episode/(num_episodes/10)))), state, deadline) 45 | 46 | if(action == int(G.nodes[1]['index'])): 47 | time_traversed = time_traversed + time_offset 48 | 49 | if ([action, time_traversed] == [None,0]): 50 | break 51 | 52 | total_time += time_traversed 53 | next_state, reward, done, _ = env.step(action, time_traversed) 54 | best_next_action = np.argmax(Q[next_state]) 55 | td_target = reward + discount_factor * Q[next_state][best_next_action] 56 | td_delta = td_target - Q[state][action] 57 | Q[state][action] += alpha * td_delta 58 | episode.append(action) 59 | if done: 60 | eps_time[i_episode] = time.time() - start_time 61 | break 62 | state = next_state 63 | 64 | row = [] 65 | filename = os.environ['resultsfile'] ## Write a result file after each episode 66 | 67 | w = csv.writer(open(filename+"Q_values.csv", "a+")) 68 | for key, val in Q.items(): 69 | row = [i_episode, key] 70 | for v in range(len(val)): 71 | row.append(val[v]) 72 | w.writerow(row) 73 | 74 | row = [] 75 | w = csv.writer(open(filename+"best_path.csv", "a+")) 76 | row = [i_episode, G.nodes[0]['name']] 77 | for state,action in Q.items(): 78 | next_node = G.nodes[np.argmax(action)]['index'] 79 | row.append(G.nodes[int(next_node)]['name']) 80 | if(next_node == G.nodes[G.number_of_nodes() - 1]['index'] ): # If it is the final node 81 | break 82 | w.writerow(row) 83 | 84 | row = [] 85 | w = csv.writer(open(filename+"chosen_path.csv", "a+")) 86 | row = [i_episode, G.nodes[0]['name']] 87 | for node in episode: 88 | row.append(G.nodes[int(node)]['name']) 89 | w.writerow(row) 90 | 91 | row = [] 92 | row = [i_episode, total_time] 93 | w = csv.writer(open(filename+"tx_times.csv", "a+")) 94 | w.writerow(row) 95 | 96 | np.savetxt(filename+"comp_times.csv", eps_time, delimiter=",") 97 | return Q, policy 98 | 99 | def make_epsilon_greedy_policy(Q, G, epsilon, observation, deadline): 100 | """ 101 | Creates an epsilon-greedy policy based on a given Q-function and epsilon. 102 | Args: 103 | Q: A dictionary that maps from state -> action-values. 104 | Each value is a numpy array of length nA (see below) 105 | epsilon: The probability to select a random action . float between 0 and 1. 106 | nA: Number of actions in the environment. 107 | Returns: 108 | A function that takes the observation as an argument and returns 109 | the probabilities for each action in the form of a numpy array of length nA. 110 | """ 111 | current_node = observation 112 | time_traversed = 0 113 | next_node = 0 114 | feasible_edges = [] 115 | for i in G[int(current_node)]: 116 | if G[int(current_node)][i]['wct'] <= deadline: 117 | feasible_edges.append(i) 118 | if(feasible_edges == []): # Are there no feasible edges 119 | print("The Schedule is not feasible") 120 | return(0,0) 121 | if(current_node == G.nodes[G.number_of_nodes() - 1]['index']): # Exit if its the last node 122 | return(0,0) 123 | probs = np.ones(len(feasible_edges), dtype = float) * epsilon / len(feasible_edges) # probabilities assignment 124 | probs = np.zeros(G.number_of_nodes()) 125 | 126 | unique_nodes = [] 127 | new_probs = epsilon / len(feasible_edges) # Divide by number of edges 128 | 129 | for i in feasible_edges: 130 | probs[feasible_edges] = new_probs 131 | if(all(np.multiply(Q[current_node],probs) == 0.0)): ## If we have no data on the value function, use a feasible edge 132 | best_action = np.argmax(probs) 133 | else: 134 | best_action = np.argmax(np.multiply(Q[current_node],probs)) # Non feasible paths become 0 135 | 136 | probs[best_action] += (1.0 - epsilon) # Add (1-epsilon) to the best action prob 137 | next_edge = np.random.choice(np.arange(len(probs)), p=probs) 138 | time_traversed = G[int(current_node)][int(next_edge)]["tx"] 139 | next_node = next_edge 140 | 141 | if(next_edge not in feasible_edges): 142 | print("NEXT EDGE NOT IN FEASIBLE") 143 | 144 | return(next_node, time_traversed) 145 | 146 | 147 | deadline = int(os.environ['deadline']) 148 | num_episodes = int(os.environ['num_episodes']) 149 | 150 | print("Dynamic Network with deadline ",deadline," with epsiodes", num_episodes) 151 | G = prsnt() 152 | best_path = [G.node[0]['name']] 153 | env = RountingEnv(G, deadline) 154 | Q, policy = mc_prediction(make_epsilon_greedy_policy, env, G, deadline, num_episodes) 155 | -------------------------------------------------------------------------------- /Code/Src/multi_nodes.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import random 3 | import os 4 | import networkx as nx 5 | import time 6 | import sys 7 | import statistics 8 | 9 | from environ import RountingEnv # The routing environment 10 | from collections import defaultdict # memory efficient storing of value functions 11 | from network_generator import prsnt, random_dag 12 | 13 | import csv 14 | 15 | def mc_prediction(policy, env,G, Final_deadline, num_episodes, discount_factor=1, alpha=1, epsilon=0.1): 16 | """ 17 | Monte Carlo prediction algorithm. Calculates the value function 18 | for a given policy using sampling. 19 | 20 | Args: 21 | policy: A function that maps an observation to action probabilities. 22 | env:Routing environment. 23 | num_episodes: Number of episodes to sample. 24 | discount_factor: Gamma discount factor. 25 | 26 | Returns: 27 | A dictionary that maps from state -> value. 28 | The state is a tuple and the value is a float. 29 | """ 30 | # Keeps track of time and count of returns for each state 31 | returns_time = defaultdict(float) 32 | returns_count = defaultdict(float) 33 | Q = defaultdict(lambda: defaultdict(float)) 34 | for node in G.nodes(): 35 | for edge in G.successors(node): 36 | Q[node][edge] = 0.0 # The final value function 37 | 38 | eps_time = np.empty(num_episodes+1) 39 | total_time = np.empty(num_episodes+1) 40 | node_num = 5 41 | for i_episode in range(1, num_episodes + 1): 42 | start_time = time.time() # Time keeping 43 | time_offset = 0 44 | episode = [] # An episode is an array of (state, action, reward) tuples 45 | total_time[i_episode] = 0 46 | state = env.reset(Final_deadline) 47 | for t in range(100): 48 | deadline = env.get_deadline() 49 | action, time_traversed = policy(Q, G, epsilon, state, deadline) 50 | total_time[i_episode] += time_traversed 51 | if ([action, time_traversed] == [None,0]): 52 | break 53 | next_state, reward, done, _ = env.step(action, time_traversed) 54 | if(not(Q[next_state])): 55 | td_target = reward 56 | else: 57 | best_next_action = max(Q[next_state],key=Q[next_state].get) 58 | td_target = reward + discount_factor * Q[next_state][best_next_action] 59 | td_delta = td_target - Q[state][action] 60 | Q[state][action] += alpha * td_delta 61 | if done: 62 | eps_time[i_episode] = time.time() - start_time 63 | break 64 | state = next_state 65 | """ 66 | row = [] 67 | filename = os.environ['resultsfile'] ## Write a result file after each episode 68 | w = csv.writer(open(filename, "a+")) 69 | for key, val in Q.items(): 70 | row = [i_episode, key] 71 | for v in range(len(val)): 72 | row.append(val[v]) 73 | w.writerow(row) 74 | 75 | row = [] 76 | bestpathfile = os.environ['bestpathfile'] ## Write the best path for each episode 77 | w = csv.writer(open(bestpathfile, "a+")) 78 | row = [i_episode, G.nodes[0]['name']] 79 | for state,action in Q.items(): 80 | next_node = G.nodes[np.argmax(action)]['index'] 81 | row.append(G.nodes[int(next_node)]['name']) 82 | if(next_node == G.nodes[G.number_of_nodes() - 1]['index'] ): # If it is the final node 83 | break 84 | w.writerow(row) 85 | """ 86 | row = [] 87 | row = [statistics.mean(total_time), Final_deadline] 88 | tx_time_file = "multinode_tx_times.csv" ## Write the time traveresed for each episode 89 | w = csv.writer(open(tx_time_file, "a+")) 90 | w.writerow(row) 91 | 92 | return Q, policy 93 | 94 | def make_epsilon_greedy_policy(Q, G, epsilon, observation, deadline): 95 | """ 96 | Creates an epsilon-greedy policy based on a given Q-function and epsilon. 97 | Args: 98 | Q: A dictionary that maps from state -> action-values. 99 | Each value is a numpy array of length nA (see below) 100 | epsilon: The probability to select a random action . float between 0 and 1. 101 | nA: Number of actions in the environment. 102 | Returns: 103 | A function that takes the observation as an argument and returns 104 | the probabilities for each action in the form of a numpy array of length nA. 105 | """ 106 | current_node = observation 107 | time_traversed = 0 108 | next_node = 0 109 | feasible_edges = [] 110 | 111 | for i in G[int(current_node)]: 112 | if G[int(current_node)][i]['wct'] <= deadline: 113 | feasible_edges.append(i) 114 | 115 | if(feasible_edges == []): # Are there no feasible edges 116 | return(0,0) 117 | 118 | if(current_node == G.nodes[G.number_of_nodes() - 1]['index']): # Exit if its the last node already 119 | return(0,0) 120 | 121 | probs = [] # probabilities assignment 122 | 123 | unique_nodes = [] 124 | new_probs = epsilon / len(feasible_edges) # Divide by number of edges 125 | empty_Q_feasible = False 126 | for edge in feasible_edges: 127 | if(Q[current_node][edge]): # If Q value exists 128 | if(Q[current_node][edge] == 0.0): 129 | empty_Q_feasible = True 130 | probs = np.full(len(feasible_edges), new_probs) 131 | 132 | if(empty_Q_feasible): 133 | best_action = np.argmax(probs) 134 | elif(Q[current_node] == {}): ## we have no values on Q function 135 | best_action = np.argmax(probs) 136 | else: 137 | best_action = np.argmax(Q[current_node]) # Non feasible paths become 0 138 | 139 | probs[best_action] += (1.0 - epsilon) # Add (1-epsilon) to the best action prob 140 | next_edge = feasible_edges[np.random.choice(np.arange(len(probs)), p=probs)] 141 | time_traversed = G[int(current_node)][int(next_edge)]["tx"] 142 | next_node = next_edge 143 | 144 | return(next_node, time_traversed) 145 | 146 | 147 | #deadline = int(os.environ['deadline']) 148 | num_episodes = int(os.environ['num_episodes']) 149 | 150 | for num_nodes in range(5,500): 151 | nodes = num_nodes 152 | edges = 50 * nodes 153 | G = random_dag(nodes,edges) 154 | deadline = 2 * nx.dijkstra_path_length(G,0,G.number_of_nodes()-1,weight='wc') 155 | 156 | print(num_nodes," node network with deadline ",deadline," with epsiodes", num_episodes) 157 | start_time = time.time() 158 | best_path = [G.node[0]['index']] 159 | env = RountingEnv(G, deadline) 160 | Q, policy = mc_prediction(make_epsilon_greedy_policy, env, G, deadline, num_episodes) 161 | for state,action in Q.items(): 162 | next_node = max(Q[state],key=Q[state].get) 163 | best_path.append(next_node) 164 | if(next_node == G.number_of_nodes() - 1): # If it is the final node 165 | break 166 | 167 | row = [] 168 | comp_time = time.time() - start_time 169 | row = [nodes, comp_time, len(best_path)] 170 | file = "multinode.csv" 171 | w = csv.writer(open(file, "a+")) 172 | w.writerow(row) 173 | -------------------------------------------------------------------------------- /Code/Scripts/Process-res.py: -------------------------------------------------------------------------------- 1 | import csv 2 | 3 | Final_deadline=[20,25,30,35,40] 4 | variance = [1,2,3,4,5] 5 | #variance = [5,6,7,8,9] 6 | 7 | for deadline in Final_deadline: 8 | folder = ("Results/novar/"+str(deadline)+"/") 9 | filename = folder+"best_path.csv" 10 | with open(filename) as csv_file: 11 | csv_reader = csv.reader(csv_file, delimiter=',') 12 | csv_writer = csv.writer(open(folder+"best_path_proc.csv", "a+")) 13 | eps = [] 14 | path = [] 15 | for row in csv_reader: 16 | eps = row[0] 17 | path = row[1:] 18 | if(path == ['i','t']): 19 | value = 0 20 | elif(path == ['i','i','t']): 21 | value = 0 22 | elif(path == ['i','i','i','t']): 23 | value = 0 24 | elif(path == ['i','x','t']): 25 | value = 1 26 | elif(path == ['i','x','y','t']): 27 | value = 2 28 | elif(path == ['i','x','z','t']): 29 | value = 3 30 | else: 31 | value = 9 32 | csv_writer.writerow([eps, value]) 33 | 34 | filename = folder+"chosen_path.csv" 35 | with open(filename) as csv_file: 36 | csv_reader = csv.reader(csv_file, delimiter=',') 37 | csv_writer = csv.writer(open(folder+"chosen_path_proc.csv", "a+")) 38 | eps = [] 39 | path = [] 40 | for row in csv_reader: 41 | eps = row[0] 42 | path = row[1:] 43 | if(path == ['i','t']): 44 | value = 0 45 | elif(path == ['i','i','i','t']): 46 | value = 0 47 | elif(path == ['i','x','t']): 48 | value = 1 49 | elif(path == ['i','x','y','t']): 50 | value = 2 51 | elif(path == ['i','x','z','t']): 52 | value = 3 53 | else: 54 | value = 9 55 | csv_writer.writerow([eps, value]) 56 | 57 | for deadline in Final_deadline: 58 | folder = ("Results/Dynamic/"+str(deadline)+"/") 59 | filename = folder+"best_path.csv" 60 | with open(filename) as csv_file: 61 | csv_reader = csv.reader(csv_file, delimiter=',') 62 | csv_writer = csv.writer(open(folder+"best_path_proc.csv", "a+")) 63 | eps = [] 64 | path = [] 65 | for row in csv_reader: 66 | eps = row[0] 67 | path = row[1:] 68 | if(path == ['i','t']): 69 | value = 0 70 | elif(path == ['i','i','t']): 71 | value = 0 72 | elif(path == ['i','i','i','t']): 73 | value = 0 74 | elif(path == ['i','x','t']): 75 | value = 1 76 | elif(path == ['i','x','y','t']): 77 | value = 2 78 | elif(path == ['i','x','z','t']): 79 | value = 3 80 | else: 81 | value = 9 82 | csv_writer.writerow([eps, value]) 83 | filename = folder+"chosen_path.csv" 84 | with open(filename) as csv_file: 85 | csv_reader = csv.reader(csv_file, delimiter=',') 86 | csv_writer = csv.writer(open(folder+"chosen_path_proc.csv", "a+")) 87 | eps = [] 88 | path = [] 89 | for row in csv_reader: 90 | eps = row[0] 91 | path = row[1:] 92 | if(path == ['i','t']): 93 | value = 0 94 | elif(path == ['i','i','i','t']): 95 | value = 0 96 | elif(path == ['i','x','t']): 97 | value = 1 98 | elif(path == ['i','x','y','t']): 99 | value = 2 100 | elif(path == ['i','x','z','t']): 101 | value = 3 102 | else: 103 | value = 9 104 | csv_writer.writerow([eps, value]) 105 | 106 | for var in variance: 107 | for deadline in Final_deadline: 108 | folder = ("Results/Var/normal/"+str(deadline)+"/"+str(var)+"/") 109 | filename = folder+"best_path.csv" 110 | with open(filename) as csv_file: 111 | csv_reader = csv.reader(csv_file, delimiter=',') 112 | csv_writer = csv.writer(open(folder+"best_path_proc.csv", "a+")) 113 | eps = [] 114 | path = [] 115 | for row in csv_reader: 116 | eps = row[0] 117 | path = row[1:] 118 | if(path == ['i','t']): 119 | value = 0 120 | elif(path == ['i','i','t']): 121 | value = 0 122 | elif(path == ['i','i','i','t']): 123 | value = 0 124 | elif(path == ['i','x','t']): 125 | value = 1 126 | elif(path == ['i','x','y','t']): 127 | value = 2 128 | elif(path == ['i','x','z','t']): 129 | value = 3 130 | else: 131 | value = 9 132 | csv_writer.writerow([eps, value]) 133 | filename = folder+"chosen_path.csv" 134 | with open(filename) as csv_file: 135 | csv_reader = csv.reader(csv_file, delimiter=',') 136 | csv_writer = csv.writer(open(folder+"chosen_path_proc.csv", "a+")) 137 | eps = [] 138 | path = [] 139 | for row in csv_reader: 140 | eps = row[0] 141 | path = row[1:] 142 | if(path == ['i','t']): 143 | value = 0 144 | elif(path == ['i','i','i','t']): 145 | value = 0 146 | elif(path == ['i','x','t']): 147 | value = 1 148 | elif(path == ['i','x','y','t']): 149 | value = 2 150 | elif(path == ['i','x','z','t']): 151 | value = 3 152 | else: 153 | value = 9 154 | csv_writer.writerow([eps, value]) 155 | 156 | for var in variance: 157 | for deadline in Final_deadline: 158 | folder = ("Results/Var/uniform/"+str(deadline)+"/"+str(var)+"/") 159 | filename = folder+"best_path.csv" 160 | with open(filename) as csv_file: 161 | csv_reader = csv.reader(csv_file, delimiter=',') 162 | csv_writer = csv.writer(open(folder+"best_path_proc.csv", "a+")) 163 | eps = [] 164 | path = [] 165 | for row in csv_reader: 166 | eps = row[0] 167 | path = row[1:] 168 | if(path == ['i','t']): 169 | value = 0 170 | elif(path == ['i','i','t']): 171 | value = 0 172 | elif(path == ['i','i','i','t']): 173 | value = 0 174 | elif(path == ['i','x','t']): 175 | value = 1 176 | elif(path == ['i','x','y','t']): 177 | value = 2 178 | elif(path == ['i','x','z','t']): 179 | value = 3 180 | else: 181 | value = 9 182 | csv_writer.writerow([eps, value]) 183 | filename = folder+"chosen_path.csv" 184 | with open(filename) as csv_file: 185 | csv_reader = csv.reader(csv_file, delimiter=',') 186 | csv_writer = csv.writer(open(folder+"chosen_path_proc.csv", "a+")) 187 | eps = [] 188 | path = [] 189 | for row in csv_reader: 190 | eps = row[0] 191 | path = row[1:] 192 | if(path == ['i','t']): 193 | value = 0 194 | elif(path == ['i','i','i','t']): 195 | value = 0 196 | elif(path == ['i','x','t']): 197 | value = 1 198 | elif(path == ['i','x','y','t']): 199 | value = 2 200 | elif(path == ['i','x','z','t']): 201 | value = 3 202 | else: 203 | value = 9 204 | csv_writer.writerow([eps, value]) 205 | 206 | for deadline in Final_deadline: 207 | folder = ("Results/uniform-wc/"+str(deadline)+"/") 208 | filename = folder+"best_path.csv" 209 | with open(filename) as csv_file: 210 | csv_reader = csv.reader(csv_file, delimiter=',') 211 | csv_writer = csv.writer(open(folder+"best_path_proc.csv", "a+")) 212 | eps = [] 213 | path = [] 214 | for row in csv_reader: 215 | eps = row[0] 216 | path = row[1:] 217 | if(path == ['i','t']): 218 | value = 0 219 | elif(path == ['i','i','t']): 220 | value = 0 221 | elif(path == ['i','i','i','t']): 222 | value = 0 223 | elif(path == ['i','x','t']): 224 | value = 1 225 | elif(path == ['i','x','y','t']): 226 | value = 2 227 | elif(path == ['i','x','z','t']): 228 | value = 3 229 | else: 230 | value = 9 231 | csv_writer.writerow([eps, value]) 232 | filename = folder+"chosen_path.csv" 233 | with open(filename) as csv_file: 234 | csv_reader = csv.reader(csv_file, delimiter=',') 235 | csv_writer = csv.writer(open(folder+"chosen_path_proc.csv", "a+")) 236 | eps = [] 237 | path = [] 238 | for row in csv_reader: 239 | eps = row[0] 240 | path = row[1:] 241 | if(path == ['i','t']): 242 | value = 0 243 | elif(path == ['i','i','i','t']): 244 | value = 0 245 | elif(path == ['i','x','t']): 246 | value = 1 247 | elif(path == ['i','x','y','t']): 248 | value = 2 249 | elif(path == ['i','x','z','t']): 250 | value = 3 251 | else: 252 | value = 9 253 | csv_writer.writerow([eps, value]) 254 | -------------------------------------------------------------------------------- /Code/Results/Dynamic/20/best_path_proc.csv: -------------------------------------------------------------------------------- 1 | 1,0 2 | 2,1 3 | 3,1 4 | 4,1 5 | 5,1 6 | 6,1 7 | 7,1 8 | 8,1 9 | 9,1 10 | 10,1 11 | 11,1 12 | 12,1 13 | 13,1 14 | 14,1 15 | 15,1 16 | 16,1 17 | 17,1 18 | 18,1 19 | 19,1 20 | 20,1 21 | 21,1 22 | 22,1 23 | 23,1 24 | 24,1 25 | 25,1 26 | 26,1 27 | 27,1 28 | 28,1 29 | 29,1 30 | 30,1 31 | 31,1 32 | 32,1 33 | 33,1 34 | 34,1 35 | 35,1 36 | 36,1 37 | 37,1 38 | 38,1 39 | 39,1 40 | 40,1 41 | 41,9 42 | 42,9 43 | 43,9 44 | 44,9 45 | 45,9 46 | 46,9 47 | 47,9 48 | 48,9 49 | 49,9 50 | 50,9 51 | 51,9 52 | 52,9 53 | 53,9 54 | 54,9 55 | 55,9 56 | 56,9 57 | 57,9 58 | 58,9 59 | 59,9 60 | 60,9 61 | 61,9 62 | 62,9 63 | 63,9 64 | 64,9 65 | 65,9 66 | 66,9 67 | 67,9 68 | 68,9 69 | 69,9 70 | 70,9 71 | 71,9 72 | 72,9 73 | 73,9 74 | 74,9 75 | 75,9 76 | 76,9 77 | 77,9 78 | 78,9 79 | 79,9 80 | 80,9 81 | 81,9 82 | 82,9 83 | 83,9 84 | 84,9 85 | 85,9 86 | 86,9 87 | 87,9 88 | 88,9 89 | 89,9 90 | 90,9 91 | 91,9 92 | 92,9 93 | 93,9 94 | 94,9 95 | 95,9 96 | 96,9 97 | 97,9 98 | 98,9 99 | 99,9 100 | 100,9 101 | 101,9 102 | 102,9 103 | 103,9 104 | 104,9 105 | 105,9 106 | 106,9 107 | 107,9 108 | 108,9 109 | 109,9 110 | 110,9 111 | 111,9 112 | 112,9 113 | 113,9 114 | 114,9 115 | 115,9 116 | 116,9 117 | 117,9 118 | 118,9 119 | 119,9 120 | 120,9 121 | 121,9 122 | 122,9 123 | 123,9 124 | 124,9 125 | 125,9 126 | 126,9 127 | 127,9 128 | 128,9 129 | 129,9 130 | 130,9 131 | 131,9 132 | 132,9 133 | 133,9 134 | 134,9 135 | 135,9 136 | 136,9 137 | 137,9 138 | 138,9 139 | 139,9 140 | 140,9 141 | 141,9 142 | 142,9 143 | 143,9 144 | 144,9 145 | 145,9 146 | 146,9 147 | 147,9 148 | 148,9 149 | 149,9 150 | 150,9 151 | 151,9 152 | 152,9 153 | 153,9 154 | 154,9 155 | 155,9 156 | 156,9 157 | 157,9 158 | 158,9 159 | 159,9 160 | 160,9 161 | 161,9 162 | 162,9 163 | 163,9 164 | 164,9 165 | 165,9 166 | 166,9 167 | 167,9 168 | 168,9 169 | 169,9 170 | 170,9 171 | 171,9 172 | 172,9 173 | 173,9 174 | 174,9 175 | 175,9 176 | 176,9 177 | 177,9 178 | 178,9 179 | 179,9 180 | 180,9 181 | 181,9 182 | 182,9 183 | 183,9 184 | 184,9 185 | 185,9 186 | 186,9 187 | 187,9 188 | 188,9 189 | 189,9 190 | 190,9 191 | 191,9 192 | 192,9 193 | 193,9 194 | 194,9 195 | 195,9 196 | 196,9 197 | 197,9 198 | 198,9 199 | 199,9 200 | 200,9 201 | 201,9 202 | 202,9 203 | 203,9 204 | 204,9 205 | 205,9 206 | 206,9 207 | 207,9 208 | 208,9 209 | 209,9 210 | 210,9 211 | 211,9 212 | 212,9 213 | 213,9 214 | 214,9 215 | 215,9 216 | 216,9 217 | 217,9 218 | 218,9 219 | 219,9 220 | 220,9 221 | 221,9 222 | 222,9 223 | 223,9 224 | 224,9 225 | 225,9 226 | 226,9 227 | 227,9 228 | 228,9 229 | 229,9 230 | 230,9 231 | 231,9 232 | 232,9 233 | 233,9 234 | 234,9 235 | 235,9 236 | 236,9 237 | 237,9 238 | 238,9 239 | 239,9 240 | 240,9 241 | 241,9 242 | 242,9 243 | 243,9 244 | 244,9 245 | 245,9 246 | 246,9 247 | 247,9 248 | 248,9 249 | 249,9 250 | 250,9 251 | 251,9 252 | 252,9 253 | 253,9 254 | 254,9 255 | 255,9 256 | 256,9 257 | 257,9 258 | 258,9 259 | 259,9 260 | 260,9 261 | 261,9 262 | 262,9 263 | 263,9 264 | 264,9 265 | 265,9 266 | 266,9 267 | 267,9 268 | 268,9 269 | 269,9 270 | 270,9 271 | 271,9 272 | 272,9 273 | 273,9 274 | 274,9 275 | 275,9 276 | 276,9 277 | 277,9 278 | 278,9 279 | 279,9 280 | 280,9 281 | 281,9 282 | 282,9 283 | 283,9 284 | 284,9 285 | 285,9 286 | 286,9 287 | 287,9 288 | 288,9 289 | 289,9 290 | 290,9 291 | 291,9 292 | 292,9 293 | 293,9 294 | 294,9 295 | 295,9 296 | 296,9 297 | 297,9 298 | 298,9 299 | 299,9 300 | 300,9 301 | 301,9 302 | 302,9 303 | 303,9 304 | 304,9 305 | 305,9 306 | 306,9 307 | 307,9 308 | 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945,2 946 | 946,2 947 | 947,2 948 | 948,2 949 | 949,2 950 | 950,2 951 | 951,2 952 | 952,2 953 | 953,2 954 | 954,2 955 | 955,2 956 | 956,2 957 | 957,2 958 | 958,2 959 | 959,2 960 | 960,2 961 | 961,2 962 | 962,2 963 | 963,2 964 | 964,2 965 | 965,2 966 | 966,2 967 | 967,2 968 | 968,2 969 | 969,2 970 | 970,2 971 | 971,2 972 | 972,2 973 | 973,2 974 | 974,2 975 | 975,2 976 | 976,2 977 | 977,2 978 | 978,2 979 | 979,2 980 | 980,2 981 | 981,2 982 | 982,2 983 | 983,2 984 | 984,2 985 | 985,2 986 | 986,2 987 | 987,2 988 | 988,2 989 | 989,2 990 | 990,2 991 | 991,2 992 | 992,2 993 | 993,2 994 | 994,2 995 | 995,2 996 | 996,2 997 | 997,2 998 | 998,2 999 | 999,2 1000 | 1000,2 1001 | -------------------------------------------------------------------------------- /Code/Results/novar/30/chosen_path_proc.csv: -------------------------------------------------------------------------------- 1 | 1,2 2 | 2,2 3 | 3,2 4 | 4,2 5 | 5,1 6 | 6,0 7 | 7,2 8 | 8,2 9 | 9,2 10 | 10,2 11 | 11,2 12 | 12,2 13 | 13,2 14 | 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