The 2024 research paper by Ezgi Korkmaz at the University College London provides a comprehensive taxonomy of generalization within deep reinforcement learning by classifying methods based on which part of the Markov Decision Process is modified. The author identifies significant challenges in the field, specifically highlighting how limited exploration and function approximation biases lead to overestimation and poor adaptability in high-dimensional spaces. By organizing diverse strategies into categories like algorithmic, state, and reward transformations, the text offers a unified framework for understanding current progress and limitations. A critical portion of the analysis focuses on the adversarial perspective, demonstrating that techniques intended to increase robustness can inadvertently harm a policy's ability to generalize to new environments. Ultimately, the source advocates for the establishment of standardized benchmarks to consistently measure how well agents perform across varying tasks and conditions. Source: 2024 A Survey Analyzing Generalization in Deep Reinforcement Learning University College London Ezgi Korkmaz https://arxiv.org/pdf/2401.02349