Learning Montezuma’s Revenge from a single demonstration
•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 reinfor...
هذا الخبر من OpenAI Blog. خبر يقدم أدوات ذكاء اصطناعي للتلخيص والترجمة والاستماع.
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 that underpins OpenAI Five.المصدر: OpenAI Blog | Source: OpenAI Blog
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This article was originally published by OpenAI Blog. Khabr is a licensed Jordanian AI-powered news platform (Registration #82086). We add editorial value through: AI-powered news analysis, automated summaries, AI audio narration, multi-language translation (Arabic, English, French, Turkish), and AI fact-checking. Our mission is to make news more accessible and understandable for Arabic-speaking audiences worldwide.
