Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks论文解析
为什么要翻译这篇文章 本人利用DNN和RNN做了深度强化学习的策略，本质上是一个基于策略梯度的强化学习，翻看这个作者的论文发现，核心思路是一模一样的，差别在于细节的处理，先说说和作者的相似之处： 用神经网络来表示一个确定性策略；状态空间是一个连续的，从神经网络的角度来讲，就是所谓的特征。这样做必须要解决的问题就是把状态到动作的映射变成一个连续可导的函数，由于在交易中，我们有最小交易单位，比
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相关文章
- 1. 论文笔记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]论文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 论文解读
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- 8. Recurrent Neural Networks 简述
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- 10. RNN(Recurrent Neural Networks)和LSTM