Reinforcement Learning with Goals

This repo hosts the code associated with my O'Reilly article, "Reinforcement Learning for Various, Complex Goals, Using TensorFlow," published on DATE.

This the code in this repository contains implementations of Deep Q-Network, and Learning to Act by Predicting the Future.

Requirements and installation

In order to run this notebook, you'll need to install:

• TensorFlow v1.0
• Jupyter
• NumPy
• Scipy
• Matplotlib
• Pillow
• Imageio
• Tmux

There are two easy ways to install these libraries and their dependencies:

Option A: use the provided Dockerfile configured for this notebook

1. Download and unzip this entire repo from GitHub, either interactively, or by entering

   git clone https://github.com/awjuliani/dfp.git

2. Open your terminal and use cd to navigate into the top directory of the repo on your machine

3. To build the Dockerfile, enter

   docker build -t dfp_dockerfile -f dockerfile .

   If you get a permissions error on running this command, you may need to run it with sudo:

   sudo docker build -t dfp_dockerfile -f dockerfile .

4. Run Docker from the Dockerfile you've just built

   docker run -it -p 8888:8888 -p 6006:6006 dfp_dockerfile bash

   or

   sudo docker run -it -p 8888:8888 -p 6006:6006 dfp_dockerfile bash

   if you run into permission problems.

5. Launch Jupyter and Tensorboard both by using tmux

   tmux
   
   jupyter notebook

   CTL+B, C to open a new tmux window, then

   cd './dfp'
   tensorboard --logdir=worker_0:'./train_0',...worker_n:'./train_n'
   

   Where n depends on number of workers used in async training.

   Once both jupyter and tensorboard are running, using your browser, navigate to the URLs shown in the terminal output (usually http://localhost:8888/ for Jupyter Notebook and http://localhost:6006/ for Tensorboard)

Option B: install Anaconda Python, TensorFlow, and other requirements

NumPy can be tricky to install manually, so we recommend using the managed Anaconda Python distribution, which includes NumPy, Matplotlib, and Jupyter in a single installation. The Docker-based method above is much easier, but if you have a compatible NVIDIA GPU, manual installation makes it possible to use GPU acceleration to speed up training.

1. Follow the installation instructions for Anaconda Python. We recommend using Python 3.6.

2. Follow the platform-specific TensorFlow installation instructions. Be sure to follow the "Installing with Anaconda" process, and create a Conda environment named tensorflow.

3. If you aren't still inside your Conda TensorFlow environment, enter it by typing

   source activate tensorflow

4. Install other requirements by entering

   pip install requirements.txt

5. Download and unzip this entire repo from GitHub, either interactively, or by entering

   git clone https://github.com/awjuliani/dfp.git

6. Use cd to navigate into the top directory of the repo on your machine

7. Launch Jupyter and Tensorboard both by using tmux

   tmux
   
   jupyter notebook

   CTL+B, C to open a new tmux window, then

   cd './dfp'
   tensorboard --logdir=worker_0:'./train_0',...worker_n:'./train_n'
   

   Where n depends on number of workers used in async training.

   Once both jupyter and tensorboard are running, using your browser, navigate to the URLs shown in the terminal output (usually http://localhost:8888/ for Jupyter Notebook and http://localhost:6006/ for Tensorboard)