Study on Genetic Algorithm Approaches to Improve an Autonomous Agent for a Fighting Game
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This document outlines a study aimed at developing an autonomous agent for fighting games using genetic algorithms. The research focuses on optimizing the agent's performance through evolutionary strategies, including configurations for crossover and mutation in a combat simulator. Preliminary results indicate that optimized bots can outperform tougher agents, with elitist versions showing particularly better outcomes.
Study on Genetic Algorithm Approaches to Improve an Autonomous Agent for a Fighting Game
- 2. Aim To create an agent/bot to win in fighting games combats. Focused on the Fighting Game AI Competition (FGAIC). (http://www.ice.ci.ritsumei.ac.jp/~ftgaic/index-2h.html) One of the few studies using Evolutionary Algorithms (Genetic Algorithms) in this scope. EvoAPPS 2021
- 3. Aim Start from a state-of-the-art agent: Mizuno (C. Yin Chu and R. Thawonmas). Fuzzy controller together with a classification method (kNN) to select the best action to perform. Depends on 10 values: distances and health levels. Optimize behavioural parameters by means of Genetic Algorithms. EvoAPPS 2021
- 4. Dare FightingICE (formerly FightingICE) Simulator used in the FGAIC. Framework to test own created AIs (agents). Two modes: time and health. 3 action buttons (kick, punch, special) and 9 directions. EvoAPPS 2021 https://www.ice.ci.ritsumei.ac.jp/~ftgaic/index-2.html
- 5. GAs scheme Individual vector of 10 values. Uniform crossover. Random mutation (one gene). Fitness (Time mode) – set of combats: ti → remaining time in combat i. sai → health of own agent in combat i. sri → health of rival agent in combat i. n → number of combats EvoAPPS 2021
- 6. GAs implemented • Generational without Elitism: o The whole population could be replaced. o Binary tournament (all individuals 1 vs 1). The best in their respective combats will remain. o Next population generated offspring + half random individuals o High exploration factor. EvoAPPS 2021
- 7. GAs implemented • Generational with Elitism: o The whole population could be replaced. o Binary tournament (all individuals 1 vs 1). The best in their respective combats will remain. o Next population best parents + best generated offspring o The best overall always survive. o Higher exploitation factor. EvoAPPS 2021
- 8. Experimental setup EvoAPPS 2021 USED AGENTS (FITNESS) BCP Dora CONFIGURATION - 20 generations - 16 individuals - 60% crossover probability - 10% mutation probability - 6 combats/fitness - Health mode - 300 health points - 10 runs TESTS (9 combats) vs Dora vs BCP vs Thunder
- 9. Experiments EvoAPPS 2021 4 best per run All vs All Best for the run Best for every run All vs All Best of experiment 01 02
- 10. Results (win rate) EvoAPPS 2021 Optimized bots are able to win to some tough agents
- 11. Results (health difference) EvoAPPS 2021 Optimized bots get more reduction of rivals’ health
- 12. Conclusions Preliminary study using GAs to optimize fighting game agents. Non-impressive results, but promising ones. Evolved bots are able to win to tough rivals and get a higher difference in health with rivals. Elitists versions perform better. EvoAPPS 2021
- 13. Future Work Implement a higher exploitation factor. Use more advanced GA schemes. Select a more suited configuration as well as specific and adapted operators (such as a better fitness function). Perform more complete tests against top-level agents from the Fighting Game AI Competition. EvoAPPS 2021
- 14. Thanks! Contact: Noelia Escalera: escaleranm@gmail.com Antonio Mora: amorag@ugr.es Pablo García: pablogarcia@ugr.es EvoAPPS 2021 Stickers: Panfri (Stickers Cloud)