Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
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- Chelsea Finn · Pieter Abbeel
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- Published 2016-03-01 authorship evidence authorship evidence
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- Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization arXiv · published 2016-03-01 · arXiv abstract page, retrieved for the candidate pack on 2026-10-28 (SHA-256 5becb575a9f4b04613cd539195aaa74f1cd4af6bfb85330c81afe16ea216ec20). Submission history as arXiv records it (UTC): v1 1 March 2016; v2 3 March 2016; v3 27 May 2016. The page serves the latest version; each version is a separate expression, and only the one a record names was read.
- Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization arXiv · published 2016-03-01 · arXiv:1603.00448v1, submitted 1 March 2016 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 9c359d5ec4d7fcaeebd68e9bf81b8780f9d88906f1d2347c91d18ba95487a9e1). The author list is the byline as printed on the first page.
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- Author: Chelsea FinnChelsea Finn is a credited author of Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization.2016-03-01 · publication · Stated in source · Supported Inspect claim & evidence ↗Chelsea Finn: person page →
- Author: Pieter AbbeelPieter Abbeel is a credited author of Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization.2016-03-01 · publication · Stated in source · Supported Inspect claim & evidence ↗Pieter Abbeel: person page →
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Cited by · 5
Recorded documents that explicitly reference this one. A citation to a work does not identify which version was read.
- Alignment for Advanced Machine Learning SystemsAlignment for Advanced Machine Learning Systems cites Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization.2016 · publication · Stated in source · Supported Inspect claim & evidence ↗Alignment for Advanced Machine Learning Systems: document page →
- Concrete Problems in AI SafetyConcrete Problems in AI Safety cites Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization.2016-06-21 · publication · Stated in source · Supported Inspect claim & evidence ↗Concrete Problems in AI Safety: document page →
- Deep reinforcement learning from human preferencesDeep reinforcement learning from human preferences cites Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization.2017-06-12 · publication · Stated in source · Supported Inspect claim & evidence ↗Deep reinforcement learning from human preferences: document page →
- Supervising strong learners by amplifying weak expertsSupervising strong learners by amplifying weak experts cites Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization.2018-10-19 · publication · Stated in source · Supported Inspect claim & evidence ↗Supervising strong learners by amplifying weak experts: document page →
- Scalable agent alignment via reward modeling: a research directionScalable agent alignment via reward modeling: a research direction cites Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization.2018-11-19 · publication · Stated in source · Supported Inspect claim & evidence ↗Scalable agent alignment via reward modeling: a research direction: document page →
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- In its own wordsThe paper presents an inverse optimal control algorithm that it states can learn arbitrary nonlinear cost functions, such as neural networks, without meticulous feature engineering.“To address the former challenge, we present an algorithm capable of learning arbitrary nonlinear cost functions, such as neural networks, without meticulous feature engineering.” arXiv v1, PDF p. 1, abstract2016-03-01 · publication · Stated in source · Supported Inspect claim & evidence ↗
- In its own wordsThe paper formulates a sample-based approximation for maximum-entropy inverse optimal control, addressing cost learning under unknown dynamics.“To address the latter challenge, we formulate an efficient sample-based approximation for MaxEnt IOC.” arXiv v1, PDF p. 1, abstract2016-03-01 · publication · Stated in source · Supported Inspect claim & evidence ↗