Some considerations on learning to explore via meta-reinforcement learning

OpenAI discusses meta-reinforcement learning as a method to improve exploration in AI agents. This approach helps agents learn how to explore new environments more efficiently by leveraging past experiences. Understanding this can lead to more adaptive and intelligent AI systems.

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PublishedSaturday, March 3, 2018 at 9:00 AMMar 3, 09:00 AM

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We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and E-RL². Results are presented on a novel environment we call "Krazy World" and a set of maze environments. We show E-MAML and E-RL² deliver better performance on tasks where exploration is important.

Opening the briefing

Some considerations on learning to explore via meta-reinforcement learning

Original article excerpt

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Opening the briefing

Some considerations on learning to explore via meta-reinforcement learning

Original article excerpt

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