rlblocks

rlblocks provides building blocks for reinforcement learning (RL) agents.

Example RL Algorithms

1. PPO (Proximal Policy Optimization) in CartPole-v1: examples/ppo.py. Run with python -m examples.ppo
2. DQN (Deep Q Learning) in CartPole-v1: examples/dqn.py. Run with python -m examples.dqn

MiniGrid Crossing Experiment

We provide an experiment in the MiniGrid environment called MiniGrid Crossing. See minigrid_crossing_experiment for more details and plots!

To run the experiment run:

python -m minigrid_crossing_experiment

Requirements

• Python 3.7 or greater

Install

1. Create a conda or virtual environment and activate it

2. Update pip and wheel in your environment:

pip install -U pip wheel

3. Clone this repository:

   git clone git@github.com:darpa-l2m/rlblocks.git
   

   or

   git clone https://github.com/darpa-l2m/rlblocks.git
   

4. Install the rlblocks package and its dependencies:

   pip install "./rlblocks"
   

To update rlblocks, pull the latest changes from the git repository and upgrade:

pip install -U .

Bug Reports and Feature Requests

Bug reports and feature requests should be made through issues on Github.

A bug report should contain:

• descriptive title
• environment (python version, operating system if install issue)
• expected behavior
• actual behavior
• stack trace if applicable
• any input parameters need to reproduce the bug

A feature request should describe what you want to do but cannot and any recommendations for how this new feature should work.

For Developers

To install rlblocks in editable mode with our development requirements:

pip install -e ".[dev]"

To run unit tests:

pytest

For running in conda environment:

python -m pytest 

To check for PEP8 compliance:

black --check rlblocks

To autoformat for PEP8 compliance:

black rlblocks

License

See LICENSE for license information.

Acknowledgments

rlblocks was created by the Johns Hopkins University Applied Physics Laboratory.

This software was funded by the DARPA Lifelong Learning Machines (L2M) Program.

The views, opinions, and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.

© 2021-2022 The Johns Hopkins University Applied Physics Laboratory LLC