Why I built the HuggingFace for RL agents — and why RL needs one
Youngseong Kim has developed a browser-based platform called Agenlus for training reinforcement learning (RL) agents. This platform aims to make RL more accessible by eliminating the need for expensive hardware and installations. Kim seeks feedback from the RL community to enhance the platform and its environments.
- ▪Agenlus allows users to train RL agents directly in their browser without the need for installation or GPU costs.
- ▪The platform aims to bridge the gap in accessibility for RL environments that typically require significant computational resources.
- ▪Kim launched Agenlus to foster a compounding knowledge ecosystem similar to what HuggingFace achieved in natural language processing and computer vision.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3957360) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Youngseong Kim Posted on May 28 Why I built the HuggingFace for RL agents — and why RL needs one #webdev #programming #beginners #ai Showcase Video If you've ever tried MineRL or OpenAI Five, you know the feeling. The environment is fascinating. The problem is hard in all the right ways. And then you check the compute requirements — and close the tab. RL has a compute problem. The most interesting environments are locked behind serious hardware.
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