hud-environment-builder

Build and evaluate RL environments for AI agents using Python-based SDK.

290|67|Updated Mar 2, 2025

Search Tags:#AI agent #reinforcement learning #HUD #coding environment #RL environment

Ready to run

npx skills add https://github.com/hud-evals/hud-python --skill hud-environment-builder

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hud-environment-builder
Source: https://github.com/hud-evals/hud-python/tree/main/docs
Command: npx skills add https://github.com/hud-evals/hud-python --skill hud-environment-builder

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires hud-python, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill allows you to create, evaluate, and train AI agents using HUD's reinforcement learning environments, facilitating the development of AI agents across various domains.

Core Features & Use Cases

RL Environment Construction: Build environments for AI agents with a variety of capabilities, including coding, computer use, browser, and robotics.
Task Design: Write tasks that prompt and grade agent actions, ensuring a structured learning process.
Evaluation & Training: Evaluate and train agents at scale using various models, including Claude, OpenAI, Gemini, and custom models.
Use Case: Create a coding environment where agents can write and execute Python code, and evaluate their performance based on the output.

Quick Start

Initialize a new environment with the command: hud init my-env --preset coding

Frequently Asked Questions about hud-environment-builder

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

FAQPage Schema

How do I build reinforcement learning environments for AI agents?▼

Build RL environments for AI agents using a Python-based SDK to define tasks, prompt actions, and grade performance across coding, computer use, browser, and robotics domains. Use the command `hud init my-env --preset coding` to scaffold a new environment.

Can I evaluate and train AI agents with models like Claude or OpenAI?▼

Evaluate and train AI agents at scale using various models including Claude, OpenAI, Gemini, and custom models. The SDK provides tools for task design and performance evaluation based on agent output within the constructed environments.

What's the best way to create a coding environment where agents can execute Python code?▼

Create a coding environment where agents write and execute Python code by initializing a preset with `hud init my-env --preset coding`. This sets up the necessary structure to evaluate agent performance based on the executed output.

Do I need Docker to run the RL environments locally?▼

Local Docker-based execution is optionally supported for running RL environments. You must install the `hud-python` package to use the SDK, but Docker is not strictly required for all configurations and task designs.

How does task design work for grading AI agent actions in RL environments?▼

Task design involves writing specific tasks that prompt and grade AI agent actions, ensuring a structured learning process. This allows you to systematically evaluate agent performance and guide training across different environment capabilities.