Implementing Deep Reinforcement Learning Models with Tensorflow + OpenAI Gym

The full implementation is available in aptsunny/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]. Environment Setup Make sure you have Homebrew installed: /usr/bin/ruby -e "$(curl -fsSL https://raw....

May 5, 2018 · 13 min · Lilian Weng

Predict Stock Prices Using RNN: Part 2

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. Dataset During the search, I found this library for querying Yahoo!...

July 22, 2017 · 9 min · Lilian Weng

Predict Stock Prices Using RNN: Part 1

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/aptsunny/stock-rnn. If you don’t know what is recurrent neural network or LSTM cell, feel free to check my previous post. One thing I would like to emphasize that because my motivation for writing this post is more on demonstrating how to build and train an RNN model in Tensorflow and less on solve the stock prediction problem, I didn’t try hard on improving the prediction outcomes....

July 8, 2017 · 12 min · Lilian Weng