The full implementation is available in lilianweng/deep-reinforcement-learning-gym
In the previous two posts, I have introduced the algorithms of many deep reinforcement learning models. Now it is the time to get our hands dirty and practice how to implement the models in the wild. The implementation is gonna be built in Tensorflow and OpenAI gym environment. The full version of the code in this tutorial is available in [lilian/deep-reinforcement-learning-gym].
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In the Part 2 tutorial, I would like to continue the topic on stock price prediction and to endow the recurrent neural network that I have built in Part 1 with the capability of responding to multiple stocks. In order to distinguish the patterns associated with different price sequences, I use the stock symbol embedding vectors as part of the input.
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This is a tutorial for how to build a recurrent neural network using Tensorflow to predict stock market prices. The full working code is available in github.com/lilianweng/stock-rnn. If you don’t know what is recurrent neural network or LSTM cell, feel free to check my previous post.
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