leroneb / reinforcement_learning_public

Our work on Reinforcement learning that we share with the rest of the world

reinforcement_learning

Experiments with Reinforcement learning.

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

---

Requirements

Docker

docker-compose

Setup

1. Install docker and docker-compose for your platfrom
2. Clone this repo and run ./start.sh in the project root folder. This starts a docker container with correct port configuration.
3. Run docker ps at command line. This prints the port forwarding information.
4. The output looks something like
5. Go to https://localhost/port_number where port_number is where 8888 is mapped to. In the above example, it is 32784.
6. You can access the notebooks from that url.
7. For the curious, we also mapped tensorboard port (6006) to a port. (In the above example, it is mapped to 32785 on your local machine). So if tensorboard is running inside your container, you could access it at https://localhost/32785
8. If you want to exec into the container and run code there, do docker exec -it container_id /bin/bash where container_id is from the output of docker ps command

Project based on the cookiecutter data science project template. #cookiecutterdatascience