讀論文-DARLA:Improving Zero-Shot Transfer in Reinforcement Learning
DARLA:Improving Zero-Shot Transfer in Reinforcement Learning這篇文章發表於ICML2017，主要講Reinforement learning算法在不同數據分佈上的遷移。 鑑於實驗室講論文討論的要求，將該論文做成了ppt，供大家分享。 總結，這篇文章關鍵點在於使用DAE將real world的state轉換爲latent state。另外，
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相關文章
- 1. 【論文閱讀】Discovering Reinforcement Learning Algorithms
- 2. 遷移學習論文閱讀：Transfer Learning via Learning to Transfer
- 3. 論文閱讀14：Reinforcement learning approach towards effective content recommendation in MOOC environments
- 4. Policy in Reinforcement Learning
- 5. 論文閱讀：A Survey on Transfer Learning
- 6. Transfer Learning[論文合集]
- 7. RL論文閱讀【四】Deep Reinforcement Learning with Double Q-learning（Double DQN）
- 8. 論文筆記：Learning how to Active Learn: A Deep Reinforcement Learning Approach
- 9. transfer learning
- 10. Policy Gradient Methods in Reinforcement Learning