論文筆記 Retrospective Analysis of the 2019MineRL Competition on Sample Efficient Reinforcement Learning
摘要 論文作者在NeurIPS2019舉辦了在利用人類先驗知識的sample-efficient強化學習上的MineRL挑戰賽。強化學習中agent和環境的交互成本較高，若一個算法可以儘量少地與環境交互，即用盡量少的樣本數量進行訓練，則該算法是sample efficient的。MineRL挑戰賽旨在發展使用協同人類示範（human demonstration）的強化學習來減少樣本數量的算法。這些
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相關文章
- 1. 論文筆記 Reinforcement Learning with Derivative-Free Exploration
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