{"schema_version":"onlylabs.public_signal.v1","title":"OpenAI Writing: Equivalence between policy gradients and soft Q-learning","description":"OpenAI writing signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/828d14a4-50b0-4ad6-abc0-a8ea0b9bce62","json_url":"https://onlylabs.fyi/signals/828d14a4-50b0-4ad6-abc0-a8ea0b9bce62/signal.json","generated_at":"2026-06-08T15:47:15.807+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/828d14a4-50b0-4ad6-abc0-a8ea0b9bce62","signal_json":"https://onlylabs.fyi/signals/828d14a4-50b0-4ad6-abc0-a8ea0b9bce62/signal.json","source":"https://openai.com/index/equivalence-between-policy-gradients-and-soft-q-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":{"radar":"https://onlylabs.fyi/data-radar","radar_json":"https://onlylabs.fyi/data-radar.json","opportunities":"https://onlylabs.fyi/opportunities","opportunities_json":"https://onlylabs.fyi/opportunities.json","lanes":[{"key":"safety","label":"Safety and policy","url":"https://onlylabs.fyi/data-radar/safety","json_url":"https://onlylabs.fyi/data-radar/safety/signals.json"}]}},"answer_pack":{"answer":"OpenAI published Equivalence between policy gradients and soft Q-learning. This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: Equivalence between policy gradients and soft Q-learning | OpenAI April 21, 2017 Publication Equivalence between policy gradients and soft Q-learning Read paper Loading….... onlylabs links this event to 1 captured evidence page and 6 related writing signals. It also maps to Safety and policy in the data-business radar.","signal_desk":"talking","source_context":{"source_url":"https://openai.com/index/equivalence-between-policy-gradients-and-soft-q-learning","source_host":"openai.com","occurred_at":"2017-04-21T07:00:00+00:00","first_seen_at":"2026-06-05T05:42:57.832854+00:00","date_source":"rss.item_date","context":null},"context_markers":[{"label":"Lab","value":"OpenAI","source":"signal"},{"label":"Signal desk","value":"talking","source":"signal"},{"label":"Source host","value":"openai.com","source":"source"},{"label":"Radar lane","value":"Safety and policy","source":"radar"},{"label":"Matched term","value":"policy","source":"radar"},{"label":"Watch term","value":"RL environments","source":"evidence"},{"label":"Watch term","value":"Eval methodology","source":"evidence"},{"label":"Watch term","value":"Infrastructure","source":"evidence"},{"label":"Watch term","value":"Safety and 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This talking signal gives public context for research themes, product direction, policy, or launch framing. High-signal details: Equivalence between policy gradients and soft Q-learning | OpenAI April 21, 2017 Publication Equivalence between policy gradients and soft Q-learning Read paper Loading….... onlylabs links this event to 1 captured evidence page and 6 related writing signals. 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Q-learning methods can be effective and sample-efficient when they work, however, it is not well-understood why they work, since empirically, the Q-values they estimate are very inaccurate. A partial explanation may be that Q-learning methods are secretly implementing policy gradient updates: we show that there is a precise equivalence between Q-learning and policy gradient methods in the setting of entropy-regularized reinforcement learning, that \"soft\" (entropy-regularized) Q-learning is exactly equivalent to a policy gradient method. We also point out a connection between Q-learning methods and natural policy gradient methods. Experimentally, we explore the entropy-regularized versions of Q-learning and policy gradients, and we find them to perform as well as (or slightly better than) the standard variants on the Atari benchmark. 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Q-learning methods can be effective and sample-efficient when they work, however, it is not well-understood why they work, since empirically, the Q-values they estimate are very inaccurate. A partial explanation may be that Q-learning methods are secretly implementing policy gradient updates: we show that there is a precise equivalence between Q-learning and policy gradient methods in the setting of entropy-regularized reinforcement learning, that \"soft\" (entropy-regularized) Q-learning is exactly equivalent to a policy gradient method. We also point out a connection between Q-learning methods and natural policy gradient methods. Experimentally, we explore the entropy-regularized versions of Q-learning and policy gradients, and we find them to perform as well as (or slightly better than) the standard variants on the Atari benchmark. 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