Evolved Policy Gradients
8a
- Publicado
- Recolectado
We’re releasing an experimental metalearning approach called Evolved Policy Gradients, a method that evolves the loss function of learning agents, which can enable fast training on novel tasks. Agents trained with EPG can succeed at basic tasks at test time that were outside their training regime, like learning to navigate to an object on a different side of the room…
overfeed.news indexa y enlaza. Publicamos un extracto breve; el artículo completo sigue en OpenAI News.
Más de OpenAI News
Inicia sesión para seguir esta fuente
How Amplitude ships product improvements in 1–2 days with OpenAI
Built primarily on OpenAI models, Amplitude’s proactive product agent, Wave, identifies what to build, helps teams ship changes with coding agents, and measures the impact.
Gondola doubles revenue as its team builds more with Codex
Gondola helps creatives get credit and get hired, with Codex raising its lead engineer’s output by 50% and ChatGPT simplifying daily work.
Holiday Extras is rethinking what it can build with Codex
With Codex, Holiday Extras is turning customer expertise into better apps, higher booking conversion and new possibilities for growth.
Supporting research on AI and children’s safety
OpenAI commits $2 million to research on AI and children’s safety. The work supports shared tools to measure risks and opportunities for young people.