1 - INTRODUCTION

    There are 4 main folders in this project:

	- Dark_Path_Algorithm:	    Contains the project where it is used the
				    Dark Path Algorithm for defining a stealthy
				    path (MARZOUQI; JARVIS). This project only 
				    allows the agent to move toward the 
				    destination.
				    
	- Genetic_Algorithm:	    Contains the Rule Based Genetic Algorithm
				    and the Table Based Genetic Algorithm;
				    
	- Reinforcement_Learning:   Contains the project with the Q-Learning,
				    Sarsa-Lambda and Dyna-Q algorithms;
				    
	- Stealthy_Path_Planning:   Contains the project where it is used the
				    navigation mesh based stealthy path planning 
				    method. This project only allows the agent to
				    move toward the destination.
				
COMPILATION GUIDELINES:
    
    - All projects uses a Makefile to compile. Therefore, to compile a 
    given project, simply go to the correct folder using a terminal and
    type 'make'. To clean the files generated through compilation, simply
    type 'make clean'.
    
EXECUTION GUIDELINES:

    - To view the execution guidelines and parameters, use the '-h' flag
    when executing the desired project in order to show a 'Help' menu with
    all possible parameters;
    
    - The best individual found by the RBGA and TBGA was placed inside the
    folder 'QValues' in all Reinforcement Learning projects. To test them,
    it is necessary to pass on of these files as a starting Q-Table (further
    directions are given in the 'Help' menu of each project inside the
    'Reinforcement_Learning' folder.
    
EXTRA:
    
    - The following links contain recordings of the stealth game simulator
    using different techniques:
	1 - Stealthy Path Planning: 	https://www.youtube.com/watch?v=2AFqYN_eoAI
	2 - Q-Learning:			https://www.youtube.com/watch?v=ZaEZbhw9974
	3 - TBGA:			https://www.youtube.com/watch?v=7ks9WgCY1FY
	4 - RBGA:			https://www.youtube.com/watch?v=6unj0Ub0d7M
	5 - Dark Path Algorithm: 	https://www.youtube.com/watch?v=RUBCG9ZuIVY