Datasetpublic

bolt-lab/POPGym-Arcade

POPGym Arcade POPGym Arcade is a GPU-accelerated Atari-style benchmark and suite of analysis tools for reinforcement learning. For more details, check out the project website. Check the documentation for the guide you need — quick start, memory introspection, or reproducing experiments. Tasks POPGym Arcade contains pixel-based tasks in the style of the Arcade Learning Environment. Each environment provides: Three difficulty settings One… See the full description on the dataset page: https://huggingface.co/datasets/bolt-lab/POPGym-Arcade.

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Dataset Card

POPGym Arcade

POPGym Arcade is a GPU-accelerated Atari-style benchmark and suite of analysis tools for reinforcement learning.

For more details, check out the project website.

Check the documentation for the guide you need — quick start, memory introspection, or reproducing experiments.

Tasks

POPGym Arcade contains pixel-based tasks in the style of the Arcade Learning Environment.  Each environment provides:

—Three difficulty settings
—One observation and action space shared across all envs
—Fully observable and partially observable configurations
—Fast and easy GPU vectorization using jax
—Standardized returns in [0,1] or [-1, 1]

Baselines

We provide a single training script for all algorithms and memory models. The `memax` library provides 18 different memory models for use in our script.

RL Algorithms

—PQN
—PPO
—DQN

Getting Started

To install the environments, run

bash

pip install popgym-arcade

If you plan to use our training scripts, install the baselines as well. If you want to play the games yourself, also use the human flag.

bash

pip install 'popgym-arcade[baselines,human]'

[!NOTE] If you do not already have jax installed, we install CPU jax by default. For GPU acceleration, run pip install jax[cuda12] after installing popgym-arcade.

Human Play

The play script installed with pip install popgym-arcade[human] lets you play the games yourself using the arrow keys and spacebar.

bash

popgym-arcade-play NoisyCartPoleEasy        # play MDP 256 pixel version
popgym-arcade-play BattleShipEasy -p -o 128 # play POMDP 128 pixel version

Other Useful Libraries

—`stable-gymnax` - A (stable) jax-capable gymnasium API
—`memax` - Recurrent models for jax
—`popgym` - The original collection of POMDPs, implemented in numpy
—`popjaxrl` - A jax version of popgym
—`popjym` - A more readable version of popjaxrl environments that served as a basis for our work

Citation

If you use POPGym Arcade in your work, please cite it as follows:

bibtex

@article{wang2025investigating,
  title={Investigating Memory in Model-Free RL with POPGym Arcade},
  author={Wang, Zekang and He, Zhe and Zhang, Borong and Toledo, Edan and Morad, Steven},
  journal={arXiv preprint arXiv:2503.01450},
  year={2025}
}