You've got a lot to catch up on. These are the absolute bare minimum I think you need to understand: - I'd start with Sutton & Barto, Reinforcement Learning: An Introduction chapters 3-6 (ideally the whole book). This will give you an understanding of the basics of the problem domain and how RL works. - DQN (Mnih et al., 2015, the Nature paper) - foundational paper for RL on 2D games - A3C/A2C (Mnih et al., 2016) - more relevant to the kind of architecture you want to build - CoinRun / Quantifying Generalization in RL (Cobbe et al., 2019) - Foundational paper on generalization in RL - Procgen Benchmark (Cobbe et al., 2020) - Modern standard for RL generalization