{"schema_version":"onlylabs.public_signal.v1","title":"OpenAI Writing: Some considerations on learning to explore via meta-reinforcement learning","description":"OpenAI writing signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/039c5a9d-f405-434a-a1c5-9e058671c85e","json_url":"https://onlylabs.fyi/signals/039c5a9d-f405-434a-a1c5-9e058671c85e/signal.json","generated_at":"2026-06-08T15:47:05.984+00:00","org":{"slug":"openai","name":"OpenAI","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/openai","dossier_json_url":"https://onlylabs.fyi/labs/openai/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/039c5a9d-f405-434a-a1c5-9e058671c85e","signal_json":"https://onlylabs.fyi/signals/039c5a9d-f405-434a-a1c5-9e058671c85e/signal.json","source":"https://openai.com/index/some-considerations-on-learning-to-explore-via-meta-reinforcement-learning","lab_dossier":"https://onlylabs.fyi/labs/openai","lab_dossier_json":"https://onlylabs.fyi/labs/openai/dossier.json","analysis":"https://onlylabs.fyi/analysis/openai","analysis_json":"https://onlylabs.fyi/analysis/openai/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/openai/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml","data_business":null},"answer_pack":{"answer":"OpenAI published Some considerations on learning to explore via meta-reinforcement learning. 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This talking signal gives public context for research themes, product direction, policy, or launch framing. 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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. - Learning Paradigms Authors Bradly Stadie, Ge Yang, Rein Houthooft, Xi Chen, Yan Duan, Yuhuai Wu, Pieter Abbeel, Ilya Sutskever Related articles View all Scaling laws for reward model overoptimizationPublicationOct 19, 2022 Learning to play Minecraft with Video PreTrainingConclusionJun 23, 2022 Dota 2 with large scale deep reinforcement learningPublicationDec 13, 2019"},"evidence_pages":[{"url":"https://openai.com/index/some-considerations-on-learning-to-explore-via-meta-reinforcement-learning","final_url":"https://openai.com/index/some-considerations-on-learning-to-explore-via-meta-reinforcement-learning","title":"Some considerations on learning to explore via meta-reinforcement learning","http_status":200,"content_type":null,"capture_method":"exa","fetched_at":"2026-06-08T15:47:05.984+00:00","bytes":null,"raw_path":null,"content_hash":null,"excerpt_chars":1200,"truncated":false,"excerpt":"Some considerations on learning to explore via meta-reinforcement learning | OpenAI March 3, 2018 Publication Some considerations on learning to explore via meta-reinforcement learning Read paper Loading… Share Abstract We consider the problem of exploration in meta reinforcement learning. 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