Improving Sample Efficiency In Model-Free Reinforcement Learning From Images 論文翻譯
文章目錄 論文原文鏈接 摘要 INTRODUCTION 相關工作 背景 剖析利用 β \beta β-變分自編碼器進行狀態表示學習的方法 實驗環境設置 沒有輔助任務的model-free off-policy RL 應用 β \beta β-自編碼器進行交替式的表示學習 β \beta β-自編碼器端到端學習的嘗試 我們的方法 在像素上的性能 簡化實驗 編碼器產生的表示的能力 泛化到沒有見過的任務
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相關文章
- 1. 【論文翻譯】Playing Atari with Deep Reinforcement Learning
- 2. 論文筆記：Learning Denoising from Single Noisy Images
- 3. 論文筆記 Retrospective Analysis of the 2019MineRL Competition on Sample Efficient Reinforcement Learning
- 4. 讀論文-DARLA:Improving Zero-Shot Transfer in Reinforcement Learning
- 5. (轉) Deep Reinforcement Learning: Pong from Pixels
- 6. 《Neural Architecture Search with Reinforcement Learning》翻譯
- 7. Policy in Reinforcement Learning
- 8. 【論文筆記】Improving Automatic Source Code Summarization via Deep Reinforcement Learning
- 9. Learning to Design Games Strategic Environments in Reinforcement Learning（部分翻譯）
- 10. 論文翻譯：Deep Progressive Reinforcement Learning for Skeleton-based Action Recognition