POPGym: Partially Observable Process Gym

🚨 Try our new GPU-accelerated, Atari-style POPGym Arcade 🚨

POPGym is designed to benchmark memory in deep reinforcement learning. It contains a set of environments and a collection of memory model baselines. The full paper is available on OpenReview.

Please see the documentation for advanced installation instructions and examples. The environment quickstart will get you up and running in a few minutes.

Quickstart Install

# Install base environments, only requires numpy and gymnasium
pip install popgym
# Also include navigation environments, which require mazelib
pip install "popgym[navigation]"

Quickstart Usage

import popgym
from popgym.wrappers import PreviousAction, Antialias, Flatten, DiscreteAction
env = popgym.envs.PositionOnlyCartPoleEasy()
print(env.reset(seed=0))
wrapped = DiscreteAction(Flatten(PreviousAction(env))) # Append prev action to obs, flatten obs/action spaces, then map the multidiscrete action space to a single discrete action for Q learning
print(wrapped.reset(seed=0))

POPGym Environments

POPGym contains Partially Observable Markov Decision Process (POMDP) environments following the Gymnasium interface. POPGym environments have minimal dependencies and are fast enough to solve on a laptop CPU in less than a day. We provide the following environments:

Feel free to rerun this benchmark using this colab notebook.

POPGym Baselines

[!WARNING] The baselines rely on difficult-to-maintain dependencies that are no longer supported. You will need to install an old version of python and downgrade some packages if you intend to use them.

POPGym baselines implements recurrent and memory model in an efficient manner. POPGym baselines is implemented on top of rllib using their custom model API. We provide the following baselines:

1. MLP
2. Positional MLP
3. Framestacking (Paper)
4. Temporal Convolution Networks (Paper)
5. Elman Networks (Paper)
6. Long Short-Term Memory (Paper)
7. Gated Recurrent Units (Paper)
8. Independently Recurrent Neural Networks (Paper)
9. Fast Autoregressive Transformers (Paper)
10. Fast Weight Programmers (Paper)
11. Legendre Memory Units (Paper)
12. Diagonal State Space Models (Paper)
13. Differentiable Neural Computers (Paper)

Contributing

Follow style and ensure tests pass

# Using uv, you can also use pip instead
uv sync --extra navigation
uv run pre-commit install
uv run pytest tests/

Citing

@inproceedings{
morad2023popgym,
title={{POPG}ym: Benchmarking Partially Observable Reinforcement Learning},
author={Steven Morad and Ryan Kortvelesy and Matteo Bettini and Stephan Liwicki and Amanda Prorok},
booktitle={The Eleventh International Conference on Learning Representations},
year={2023},
url={https://openreview.net/forum?id=chDrutUTs0K}
}