Browsing by Author "Gomes, Gabriel Quintas"
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- The world of Anwin : reinforcement learning in role-playing gamesPublication . Gomes, Gabriel Quintas; Pereira, Francisco José Batista; Rocha, Teresa Raquel Corga Teixeira daThe video game industry is vast and fast expanding, generating more revenue than the film and music industries combined. Likewise, the capabilities of machine learning algorithms keep improving and broadening, allowing them to recognize and understand increasingly complex patterns and, in some cases, respond accordingly. With this versatility and the prevalence of both areas, it is interesting to study different implementations of these algorithms and apply the findings to a new custom-built video game, specially made for this purpose. In this project, using the Unreal Engine 5, a video game was created with the goal of implementing a Reinforcement Learning model. The game will be a light Role-Playing Game with a story that can guide the player on a short journey. Many new tools were learned, and systems built to create an environment where the game could be built, all from the perspective of a novice video game developer. The combat system has a prominent role, with its agents all powered by the Reinforcement Learning that will be responsible for all decision the agent will make. The least developed side of current games being published is their Artificial Intelligence implementation, with this possibly being a viable alternative. In the end, it is important to achieve a good symbiosis between the game and the model built, to prove that an idea like this could be implemented on more mainstream games.