Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks論文解析
爲什麼要翻譯這篇文章 本人利用DNN和RNN做了深度強化學習的策略，本質上是一個基於策略梯度的強化學習，翻看這個作者的論文發現，核心思路是一模一樣的，差別在於細節的處理，先說說和作者的相似之處： 用神經網絡來表示一個確定性策略；狀態空間是一個連續的，從神經網絡的角度來講，就是所謂的特徵。這樣做必須要解決的問題就是把狀態到動作的映射變成一個連續可導的函數，由於在交易中，我們有最小交易單位，比
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相關文章
- 1. 論文筆記12:Building Adaptive Tutoring Model using Artificial Neural Networks and Reinforcement Learning
- 2. PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
- 3. Recurrent Neural Networks VS LSTM
- 4. QUANT[22]論文2:Deep Direct Reinforcement Learning for Financial Signal Representation and Trading
- 5. Attention and Augmented Recurrent Neural Networks
- 6. CVPR 2017 On human motion prediction using recurrent neural networks 論文解讀
- 7. 論文解讀A Critical Review od Recurrent Neural Networks for Sequence Learning
- 8. Recurrent Neural Networks 簡述
- 9. (zhuan) Recurrent Neural Network
- 10. RNN(Recurrent Neural Networks)和LSTM