awjuliani / oreilly-rl-tutorial

Contains Jupyter notebooks associated with the "Deep Reinforcement Learning Tutorial" tutorial given at the O'Reilly 2017 NYC AI Conference.

Deep Reinforcement Learning Tutorial

Contains Jupyter notebooks associated with the Deep Reinforcement Learning Tutorial given at the O'Reilly 2017 NYC AI Conference. Slides from the presentation can be downloaded here.

Required Unity Environments can be downloaded here. Download and unzip the .zip file associated with your OS (ie Linux, Mac, or Windows) and move each of the files within the unzipped folder (ie 2DBall, 3DBall, etc) to the root directory of this repository.

Requirements

• Tensorflow (version 1.0+)
• Pillow
• Matplotlib
• numpy
• scipy
• Jupyter

To install dependencies, run:

pip install -r requirements.txt

or

pip3 install -r requirements.txt

If your Python environment doesn't include pip, see these instructions on installing it.

Training RL Agents

To launch jupyter, run:

jupyter notebook

Then navigate to localhost:8888 to access each training notebook.

To monitor training progress, run the following from the root directory of this repo:

tensorboard --logdir='./summaries'

Then navigate to localhost:6006 to monitor progress with Tensorboard.

Troubleshooting

macOS Permission Error

If you recieve a permission error when attempting to launch an environment on macOS, run:

chmod -R 755 *.app

Filename not found

If you recieve a file-not-found error while attempting to launch an environment, ensure that the environment files are in the root repository directory. For example, if there is a sub-folder containing the environment files, those files should be removed from the sub-folder and moved to the root.