LIST OF FIGURESSYMBOLS AND ABBREVIA

LIST OF FIGURES
SYMBOLS AND ABBREVIATIONS
CHAPTER
I INTRODUCTION
1.1 Significance of the Problem
1.2 Research Objective
1.3 Assumptions
1.4 Scope
1.5 Expected Usefulness
1.6 Synopsis of Thesis
II BACKGROUND THEORY
2.1 Markov Processes
2.1.1 Discrete-Time Markov Chain
2.1.2 Markov Decision Process
2.2 Reinforcement Learning
TABLE OF CONTENTS (Continued)
Page
2.2.1 Monte Carlo Method
2.2.2 Monte Carlo Estimation of Action Values
2.2.3 Monte Carlo Control
2.3 On-Policy Monte Carlo Method
III SECURE ROUTING IN MANETS : A REINFORCEMENT
LEARNING PROBLEM
3.1 Introduction
3.2 Reputation Method
3.3 Reputation as a Reinforcement Learning Problem
3.4 Problem Formulation
3.5 Experimental Results
3.5.1 Accumulated Reward per Episode
3.5.2 Number of Packets Arrived at the Destination
3.5.3 Relative Throughput
3.5.4 Effect of Varying the Maximum Allowed Packets
3.6 Conclusions
IV PERFORMANCE STUDY OF RL─BASED SECURE
ROUTING IN MANETS UNDER M/M/1/K MODEL
4.1 Introduction