14 lines
1.1 KiB
Markdown
14 lines
1.1 KiB
Markdown
---
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layout: single
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title: "Policy Entropy for OOD Classification"
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categories: research
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excerpt: "PEOC for reliably detecting unencountered states in deep RL"
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header:
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teaser: assets/figures/6_ood_pipeline.jpg
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---
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{:style="display:block; width:45%" .align-right}In this work, the development of PEOC, a policy entropy-based classifier for detecting unencountered states in deep reinforcement learning, is proposed. Utilizing the agent's policy entropy as a score, PEOC effectively identifies out-of-distribution scenarios, crucial for ensuring safety in real-world applications. Evaluated against advanced one-class classifiers within procedurally generated environments, PEOC demonstrates competitive performance.
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Additionally, a structured benchmarking process for out-of-distribution classification in reinforcement learning is presented, offering a comprehensive approach to evaluating such systems' reliability and effectiveness. {% cite sedlmeier2020policy %}
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{:style="display:block; width:90%" .align-center}
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