RL_Envs_101: Create RL Training Environments Across Frameworks

Now anyone can create RL environments for training. For this, a skill was developed — RL_Envs_101

- You can create environments in several frameworks, such as OpenEnv, OpenReward, Verifiers, NemoGym, etc.
- the repository contains live working examples of environments that your coding agent can reference
- the skill is designed from the start to determine what type of model you are training and, with that in mind, create the environment

ps. There are many more aspects to creating RL environments for training. One of the key ones is the data, which this skill does not address directly. However, the skill helps implement tools, rewards, and other components of an RL environment, simplifying the transition from idea to implementation and enabling faster solution assembly across different frameworks.

But this is still a very early version of the work and will most likely change significantly.

Installation: $ npx skills add adithya-s-k/RL_Envs_101

btw: the repo adithya-s-k/RL_Envs_101github.com/adithya-s-k/RL_Envs_101for contributions to the project and suggestions for improvements.