Reinforcement Learning, Fast and Slow
Reinforcement Learning, Fast and Slow 摘要： 深度強化學習已經取得很大成就，但是最大的缺陷在於樣本數據的有效性低。主要有兩種方法來解決這個問題： Episode Deep RL Meta RL 深度強化學習樣本數據的有效性低的原因 梯度下降。需要對參數進行迭代更新直到收斂。學習率不能太大否則無法收斂，學習率太小則收斂速度慢。 弱偏置假設。機器學習模型都是要設定
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相關文章
- 1. Reinforcement learning and Deep learning
- 2. Looking Fast and Slow: Memory-Guided Mobile Video Object Detection
- 3. Reinforcement learning: integrating learning and planning, exploitation and exploration
- 4. Reinforcement Learning Note: Concept and MDP
- 5. Fast deep reinforcement learning using online adjustments from the past
- 6. 《Distantly Supervised NER with Partial Annotation Learning and Reinforcement Learning》
- 7. Fast Slow RNN ——譯文
- 8. 視頻目標檢測Looking Fast and Slow: Memory-Guided Mobile Video Object Detection
- 9. Deep Reinforcement Learning
- 10. RLChina_Lecture01_《Introduce to Reinforcement Learning and Value-based Methods》_notebook