Reinforcement learning for AI players in turn-based RPGs
Rehman Faciabén, Mohammad Haroon · UPCommons institutional repository (Universitat Politècnica de Catalunya) · 2025
The Facultat d’Informàtica de Barcelona (FIB) is an institution known for its knowledge in computer science, using the knowledge learned in this degree, this project aims to develop an artificial intelligence capable of competing in turn-based games, such as Pokémon, using reinforcement learning techniques. The specialization of computing helps the students to possess the knowledge and skills to design adequate algorithmic strategies, such as Monte Carlo or Minmax which will be very helpful in developing bots that will train the AI. To fully understand the scope of this study, it is essential to define the terms and concepts related to artificial intelligence, machine learning and reinforcement learning. "We define AI as the study of agents that receive percepts from the environment and perform actions. Each such agent implements a function that maps percept sequences to actions, and we cover different ways to represent these functions, such as reactive agents, real-time planners, and decision-theoretic systems." [1] "Machine learning is essentially a form of applied statistics with increased emphasis on the use of computers to statistically estimate complicated functions and a decreased emphasis on proving confidence intervals around these functions." [2] "Reinforcement learning is learning what to do—how to map situations to actions—so as to maximize a numerical reward signal. The learner is not told which actions to take, but instead must discover which actions yield the most reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward but also the next situation and, through that, all subsequent rewards. These two characteristics—trial-and-error search and delayed reward—are the two most important distinguishing features of reinforcement learning."