Learning Montezuma’s Revenge from a single demonstration
8y
- Published
- Collected
We’ve trained an agent to achieve a high score of 74,500 on Montezuma’s Revenge from a single human demonstration, better than any previously published result. Our algorithm is simple: the agent plays a sequence of games starting from carefully chosen states from the demonstration, and learns from them by optimizing the game score using PPO, the same reinforcement learning algorithm…
overfeed.news indexes and links. We publish a short excerpt — the full article stays at OpenAI News.
More from OpenAI News
Log in to follow this source
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.