I just shipped CrossLearn – a lightweight, extractor-first library for reinforcement learning.

Instead of re-implementing full RL algorithms, it focuses on reusable observation encoders that work seamlessly with both a simple native REINFORCE implementation and Stable-Baselines3 (PPO, etc.).

What’s inside:

• Vector observations: FlattenExtractor for classic control tasks (CartPole, LunarLander).
• Image observations: AtariPreprocessor + NatureCNNExtractor for Atari-style environments (works with native REINFORCE or SB3 CnnPolicy).
• Time-series / trading: ChronosExtractor (online) and ChronosEmbedder (offline) using Amazon’s Chronos-2 foundation model. Great for rolling OHLCV windows in trading environments like gym-anytrading.

You can use the exact same extractor with native REINFORCE or drop it into SB3 via policy_kwargs={“features_extractor_class”: ChronosExtractor, …}.

There are 5 Colab notebooks ready to run in the repo for quick experimentation.

Repo: https://github.com/cpohagwu/crosslearn

Notebooks are linked directly in the README.

Would love your feedback – especially from folks working on trading/sequential decision-making or anyone who’s tried foundation models (like Chronos) as RL backbones.

Let me know what you think or if you’d like to see support for other time-series models or vision extractors next!