r/reinforcementlearning

I love making hard things intuitive. I hope you enjoy this one!

Let me know if you have any questions.

This technique is called neuroevolution: training a neural network through evolutionary methods like selection and mutation, without gradient descent.

https://en.wikipedia.org/wiki/Neuroevolution

We rewrote the printed parts of Open Duck Mini v2 as CadQuery (Python) programs. Then we changed three values in one file. The leg and neck parts aren't copies. They run the Duck's own programs with the new values, so the servo pockets and screw holes stay where the STS3215 needs them and only the length between them changes. We added wings and a small bird head, and the result is the Flamingo.

The repo also has SO-101 and the LeRobot humanoid as code (Apache-2.0.

https://github.com/yotown/gym

Greetings. I'm sharing what I'm doing for my doctoral thesis. If you're interested in more information, please contact me. I'm willing to collaborate.

I am using stable baseline 3 and pybullet

I trained in Genesis, then validated in MuJoCo (mjlab) before deploying.
It can recover from pushes and tracks forward, lateral and yaw velocity commands.
Happy to answer any questions!!

Hi folks, it's Lewis here from the post-training team at Hugging Face. We've been exploring how to train open models in different coding harnesses and wrote up a looong guide on how we solved this using open source libraries like TRL and the Harbor framework for RL environments. We hope you find this interesting, especially since everyone nowadays has their own custom harness (e.g. Pi + extensions) and now there's a recipe on how to squeeze the best performance on them with whatever open model you use as your daily driver. Happy to hear any comments or feedback!

Link to the guide: https://huggingface.co/spaces/FineEnvs/multi-harness-rl

I tested Adapt-1, a non-LLM learning and reasoning system by Rei Labs, by having it learn and play Super Mario Bros, and it performed quite well.

I tried it on World 1-1, starting untrained. It learned a reactive policy from its own play in about 36 minutes of gameplay, then cleared the level with learning off.

With Machina, Adapt-1's sequence engine. Starting untrained, it found a button sequence that reaches the flag after 403 attempts, in 11 wall-clock minutes.

Full thread: https://x.com/hsrvc_/status/2106025501752234112?s=20

Code, the exact data, traces, clips and a step-by-step guide with costs are all public: https://github.com/hsrvc/adapt1-mario

If you work on imitation learning or multi-agent behaviour: we just released 9,515 bot-played CS:GO matches. Per 16 Hz step for each of ten agents: actions (buttons + mouse), state (pose, health, weapon…) and who-hit-whom damage. 218,768 rounds, ≈3,958 h of play. 14% of the matches also have the ten first-person videos, aligned frame by frame.

Expert bots on one map with pistols, so it is a clean, narrow testbed rather than human play. Parquet + webdataset videos, CC BY-NC 4.0: https://huggingface.co/datasets/v0rt3ch/cs-5v5-bots-aim\_usp-3k · spec and a round you can watch from all ten POVs: https://vor.tech/?utm\_source=reddit&utm\_medium=social&utm\_campaign=launch-2026-10&utm\_content=r-reinforcementlearning