論文閱讀15：Massively Parallel Methods for Deep Reinforcement Learning
論文閱讀筆記之Massively Parallel Methods for Deep Reinforcement Learning 本文創新點：提出了一個對於深度強化學習的大規模分佈式架構Gorila（General Reinforcement learning Architecture） 架構組成：用於產生behavior的並行actor組； 從經驗池學習的並行learner組； 代表值函數
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相關文章
- 1. 【5分鐘 Paper】Asynchronous Methods for Deep Reinforcement Learning
- 2. 論文筆記之：Asynchronous Methods for Deep Reinforcement Learning
- 3. Deep Reinforcement Learning for Dialogue Generation閱讀筆記
- 4. 論文閱讀：DRN: A Deep Reinforcement Learning Framework for News Recommendation
- 5. RL論文閱讀【三】Dueling Network Architectures for Deep Reinforcement Learning
- 6. 論文閱讀：(LIRD)Deep Reinforcement Learning for List-wise Recommendations
- 7. Deep Reinforcement Learning for Dialogue Generation 論文閱讀 A Diversity-Promoting Objective Function fo
- 8. 論文閱讀-《Learning Deep Features for Discriminative Localization》
- 9. 論文閱讀：《Wide & Deep Learning for Recommender Systems》
- 10. 【論文閱讀】Deep Residual Learning for Image Recognition