<ul><li>Behavioral metric learning is a key approach to state abstraction in reinforcement learning environments.</li><li>Accurately estimating these metrics remains challenging, and prior evaluations mainly focused on final returns.</li><li>A study evaluated five recent metric learning approaches in deep reinforcement learning across various tasks and noise settings.</li><li>The study introduced a denoising factor evaluation and an isolated metric estimation setting, and released an open-source codebase for future research.</li></ul>

Understanding Behavioral Metric Learning: A Large-Scale Study on Distracting Reinforcement Learning Environments

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