| """Short and sweet LSTM implementation in Tensorflow. | |
| Motivation: | |
| When Tensorflow was released, adding RNNs was a bit of a hack - it required | |
| building separate graphs for every number of timesteps and was a bit obscure | |
| to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`. | |
| Currently the APIs are decent, but all the tutorials that I am aware of are not | |
| making the best use of the new APIs. | |
| Advantages of this implementation: |
| # Author: Kyle Kastner | |
| # License: BSD 3-Clause | |
| # Implementing http://mnemstudio.org/path-finding-q-learning-tutorial.htm | |
| # Q-learning formula from http://sarvagyavaish.github.io/FlappyBirdRL/ | |
| # Visualization based on code from Gael Varoquaux [email protected] | |
| # http://scikit-learn.org/stable/auto_examples/applications/plot_stock_market.html | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from matplotlib.collections import LineCollection |
Here are instructions to set up TensorFlow dev environment on Docker if you are running Windows, and configure it so that you can access Jupyter Notebook from within the VM + edit files in your text editor of choice on your Windows machine.
First, install https://www.docker.com/docker-toolbox
Since this is Windows, creating the Docker group "docker" is not necessary.