🧬 Evolutionary EvoMan player - Assignment 2 🎮

This repository contains a project realized as part of the Evolutionary Computing course of the Master's degree in Artificial Intelligence, Vrije Universiteit Amsterdam.
The aim of this project is compare two Evolutionary Algorithms for the task of video game playing using a python framework called EvoMan. The two EA methods train a generalist agent that should be able to play agains the different 8 enemies of the game. For more details, read the task assigment. The proposed solution is describer in the report.

Approach 1

For the first part of the experiment, meaning the evolution part, you need to:

• Run the ea1/genetic_optimization.py file. There you can set up the ENEMY global variable to change through different group of enemies.
• The best individuals found are stored in the ea1/runs/enemy_#/best_individual_run_#.txt file. The history of the evolution is stored in the ea1/runs/enemy_#/logbook_run_#.csv file.
• The ea1/hyperparameter_tuning.py file is used to tune the hyperparameters of the algorithm through hyperopt. The history of the tuning is stored in the ea1/hyperparameter_tuning/logbook_tuning.csv file.
• ea1/experiment_runner.py runs 10 optimizations simultaneously.

For the second part, where the best individuals are confronted to different enemies, you need to:

• Run the ea1/play_with_best.py file, selecting the enemy you want to play with.
• Results are then stored in the ea1/runs/enemy_#/games_played.csv file.

Approach 2

For the first part of the experiment, meaning the evolution part, you need to:

• Run the ea2/genetic_optimization.py file. There you can set up the ENEMY global variable to change through different group of enemies.
• The best individuals found are stored in the ea2/runs/enemy_#/best_individual_run_#.txt file. The history of the evolution is stored in the ea1/runs/enemy_#/logbook_run_#.csv file.
• The ea2/hyperparameter_tuning.py file is used to tune the hyperparameters of the algorithm through hyperopt.
• ea21/experiment_runner.py runs 10 optimizations simultaneously.

For the second part, where the best individuals are confronted to different enemies, you need to:

• Run the ea2/play_with_best.py file, selecting the enemy you want to play with.
• Results are then stored in the ea2/runs/enemy_#/games_played.csv file.

Experiments

Group of enemies trained on:

• [2, 5, 8] (tuned on these)
• [1, 5, 6]

Results

The best individual found is ea2/runs/enemy_1_5_6/best_individual_run_5.txt, it is trained on the group of enemies [1, 5, 6] and it is able to defeat up to 5 enemies. To have a deeper insight on the graphs, check out the plots folder.

Group members - 88

Name	Surname	Email	Username
Simone	Montali	s.montali@student.vu.nl	montali
Giuseppe	Murro	g.murro@student.vu.nl	gmurro
Nedim	Azar	n.azar@student.vu.nl	nedimazar
Martin	Pucheu Avilés	m.i.pucheuaviles@student.vu.nl	martinpucheuaviles

License

This project is licensed under the GNU General Public Licens - see the LICENSE file for details