Implementation of reinforcement learning architecture to augment an AI that can self-learn to play video games

Abstract

This paper is intended to be a practical guide in terms of getting up and running with reinforcement learning. Ideally, it aims to bridge the gap between practi cal implementation and the theories available for RL. The theory of reinforcement learning involves two main components: an environment, which is the game itself and an agent, which performs an action based on its observation from the environ ment. Initially, no in-game rules will be given to the agent and it will be rewarded or punished based on the action that it will take. The goal is to increase Proximal Policy Optimization (PPO) to maximize the reward that our agent will get, so over time it will learn what action to take in order to do so. Therefore, we will develop an AI agent that will be able to learn how to play one of the most popular arcade games of all time, Street Fighter. We preprocess our game environment and apply hyperparameter tuning using PyTorch, Stable Baselines, and Optuna to do it. This approach will basically train different types of RL architecture and find a model with the most weighted parameters. Moreover, we are going to Fine Tune that model and run our test cases on it. We are going to see how a reinforcement learning algorithm learns to play.