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README
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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