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.