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autorl-onboard-environment

Integrates reinforcement learning environments and generates machine-readable usage contracts.

Updated Jul 9, 2026
Search Tags:#integration#reinforcement learning#machine-readable#environment onboarding#autorl#usage contract
One-click install
npx skills add https://github.com/Lingjie-wang/autoRL --skill autorl-onboard-environment-lingjie-wang
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: autorl-onboard-environment
Source: https://github.com/Lingjie-wang/autoRL/tree/main/autorl-skill-workflow-v2/skills/autorl-onboard-environment
Command: npx skills add https://github.com/Lingjie-wang/autoRL --skill autorl-onboard-environment-lingjie-wang

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Onboarding a reinforcement learning environment into an automated RL workflow requires manually documenting its observation spaces, action spaces, reward signals, and reset semantics, which is error-prone and inconsistent across projects. ## Core Features & Use Cases - Environment Integration: Connects an RL environment to the AutoRL workflow so agents can interact with it through a standardized interface. - Contract Generation: Produces a machine-readable contract describing the environment's inputs, outputs, and usage rules. - Usage Guide Creation: Generates documentation explaining how to load, reset, and step through the environment. - Use Case: When adding a new Gymnasium-style environment to an AutoRL training pipeline, use this Skill to verify the environment conforms to expectations and emit its contract and usage guide automatically. ## Quick Start Use the autorl-onboard-environment skill to integrate my RL environment and produce its machine-readable contract and usage guide.

Frequently Asked Questions about autorl-onboard-environment

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I onboard a new RL environment into an AutoRL pipeline?▼

Point the skill at your environment implementation and it will inspect the environment's interface, verify it behaves as expected, and generate a machine-readable contract plus a usage guide for downstream agents.

What is a machine-readable environment contract in reinforcement learning?▼

It is a structured description of an RL environment's observation space, action space, reward signal, and reset semantics. Training agents and orchestration tools read this contract to interact with the environment without manual configuration.

Can I use this with custom environments that are not Gymnasium-based?▼

Yes, the onboarding workflow targets RL environments generally, but custom environments must expose a consistent interface for stepping, resetting, and describing their spaces so the contract can be generated accurately.

What happens if my RL environment fails verification during onboarding?▼

The onboarding process checks that the environment conforms to expected usage patterns before producing the contract. Environments that fail verification should be fixed so their step, reset, and space definitions behave consistently before retrying.

What are the limitations of automated RL environment onboarding?▼

Automated onboarding verifies interface conformance but cannot judge whether reward design or environment dynamics are suitable for learning. Reward shaping and task difficulty still require manual analysis by the RL practitioner.

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