OpenAI Blog · Jul 4, 2018
Learning Montezuma’s Revenge from a single demonstration
Reviewed by Errol Vogt, Site support technician & online learning analyst · original summary · editorial policy
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 reinforcement learning algorithm that underpins OpenAI Five. This update is relevant for small-office operators tracking changes in their tools.
Operator takeaway: For operators: review whether 'Learning Montezuma’s Revenge from a single demonstration' affects your current setup before relying on it in production.
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