Omkar2118/datasets huggingface dataset:
POPGym
Full name: Partially Observable Process Gym

POPGym is designed to benchmark memory in deep reinforcement learning. It contains a set of *environments* and a collection of *memory model baselines*. The environments are all Partially Observable Markov Decision Process (POMDP) environments following the [Openai Gym](https://github.com/openai/gym) interface. Our environments follow a few basic tenets:

1. **Painless Setup** - `popgym` environments require only `gym`, `numpy`, and `mazelib` as dependencies
2. **Laptop-Sized Tasks** - Most tasks can be solved in less than a day on the CPU 
3. **True Generalization** - All environments are heavily randomized.

The paper uses 15M environment steps for each trial.