A 2024 Survey Analyzing Generalization in Deep Reinforcement Learning
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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
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