Deep Reinforcement Learning Tutorial

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

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.

All notebooks and environments tested with Python2 and Python3 on macOS Sierra.

Requirements

• Tensorflow
• 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.