Reinforcement Learning using the Gym Retro platform

The Gym Retro framework enables implementation of reinforcement learning in classic video games. In this project we will explore the creation of automatic playing agents that learn to play some of these games using Deep Reinforcement Learning techniques. We will also explore techniques that allow us to intuitively explain the decisions taken by the trained models. All of this is done by first giving an overview of the theory behind the algorithms and tools and then applying them in increasingly complex environments. We start with the CartPole problem, a very basic environment typically used as an introduction to reinforcement learning, and we use both a symbolic and a subsymbolic approach. Once we consider that we have solved that problem, we repeat the process with the Space Invaders Atari environment, which is a much more complex task. Finally, we try to use all the previous knowledge in order to train an AI that plays Space Invaders in its NES version, which is a slightly newer console than the Atari 2600 and should be a small step up. For each of the AIs that learn through vision, we test our RISE-based explainability solution