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This repository has been deprecated in favor of the Retro (https://github.com/openai/retro) library. See our Retro Contest (https://blog.openai.com/retro-contest) blog post for detalis. universe-starter-agent The codebase implements a starter agent that can solve a number of environments. It contains a basic implementation of the A3C algorithm, adapted for real-time environments. Dependencies Python 2.7 or 3.5 Golang six (for py2/3 compatibility) TensorFlow 0.12 tmux (the start script opens up a tmux session with multiple windows) htop (shown in one of the tmux windows) gym gym[atari] libjpeg-turbo () universe opencv-python numpy scipy Getting Started conda create --name universe-starter-agent python=3.5 source activate universe-starter-agent brew install tmux htop cmake golang libjpeg-turbo # On Linux use sudo apt-get install -y tmux htop cmake golang libjpeg-dev pip install "gym[atari]" pip install universe pip install six pip install tensorflow conda install -y -c https://conda.binstar.org/menpo open
| Stars | 1,096 |
| Forks | 314 |
| Language | Python |
| Category | AI Tool |
| License | MIT |
| Quality Score | 62.8115068884727/100 |
| Last Updated | 2018-04-07 |
| Created | 2016-11-17 |
| Platforms | python |
| Est. Tokens | ~159k |
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universe-starter-agent is A starter agent that can solve a number of universe environments.. It is categorized as a AI Tool with 1.1k GitHub stars.
universe-starter-agent is primarily written in Python.
You can find installation instructions and usage details in the universe-starter-agent GitHub repository at github.com/openai/universe-starter-agent. The project has 1.1k stars and 314 forks, indicating an active community.
universe-starter-agent is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to universe-starter-agent on Agent Skills Hub include vision-agent, axton-obsidian-visual-skills, claudekit-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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