
The author introduces CMZ-DRIL as a novel imitation learning algorithm that leverages ensemble-based uncertainty quantification to improve agent performance without the need for environment-specific rewards.


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Continuous-Mean-Zero-Disagreement-Regularized-Imitation-Learning-CMZ-DRIL-Study


Continuous Mean-Zero Disagreement-Regularized Imitation Learning (CMZ-DRIL) Study


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CMZ-DRILは、専門家のデモンストレーションにアクセスする模倣学習エージェントのパフォーマンスを向上させるために新しい報酬構造を使用する。


cmz-dril-continuous-mean-zero-disagreement-regularized-imitation-learning


CMZ-DRIL: Continuous Mean-Zero Disagreement-Regularized Imitation Learning



CMZ-DRIL은 환경별 보상 함수를 사용하지 않고 전문가 데모에 액세스하는 모방 학습 에이전트의 성능을 향상시키는 혁신적인 보상 구조를 활용합니다.
