1.1 KiB
layout, title, categories, excerpt, header
| layout | title | categories | excerpt | header | ||
|---|---|---|---|---|---|---|
| single | Policy Entropy for OOD Classification | research | PEOC for reliably detecting unencountered states in deep RL |
|
{: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.
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 %}
{:style="display:block; width:90%" .align-center}