Cited by 3,119Reinforcement Learning in RoboticsAI-based Problem Solving and PlanningMachine Learning and Algorithms

Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning

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Sutton, Richard S., Precup, Doina, Singh, Satinder·1999·Artificial Intelligence

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Crossref · 10.1016/s0004-3702(99)00052-1

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74 of 74 references verified0 citation errors · 0 not checkable

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  • In robotics, skills are often modelled using the Option framework.

    “In robotics, skills are often modelled using the Option framework [78].”

    From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [78]

    Section 2 (Options; page unavailable) defines options as temporally extended actions and gives robot docking and robot-arm control as examples. This supports modelling robotic skills as options, but the examples do not establish that robotics skills are often modelled this way. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Flagged first, then by citations

1 of the 75 references couldn’t be matched to a known paper.

Research works printed but not linked: 8

  1. E.G. Araujo, R.A. Grupen, Learning control composition in a complex environment, in: Proc. 4th International Conference on Simulation of Adaptive Behavior, 1996, pp. 333-342.
  2. S. Mahadevan, N. Marchalleck, T. Das, A. Gosavi, Self-improving factory simulation using continuous-time average-reward reinforcement learning, in: Proc. 14th International Conference on Machine Learning, 1997, pp. 202-210.
  3. D. Precup, R.S. Sutton, Multi-time models for reinforcement learning, in: Proc. ICML'97 Workshop on Modeling in Reinforcement Learning, 1997.
  4. D. Precup, R.S. Sutton, S.P. Singh, Planning with closed-loop macro actions, in: Working Notes 1997 AAAI Fall Symposium on Model-directed Autonomous Systems, 1997, pp. 70-76.
  5. S. Sastry, Algorithms for design of hybrid systems, in: Proc. International Conference of Information Sciences, 1997.
  6. S.P. Singh, Scaling reinforcement learning by learning variable temporal resolution models, in: Proc. 9th International Conference on Machine Learning, Morgan Kaufmann, San Mateo, CA, 1992, pp. 406-415.
  7. R.S. Sutton, B. Pinette, The learning of world models by connectionist networks, in: Proc. 7th Annual Conference of the Cognitive Science Society, 1985, pp. 54-64.
  8. C.J.C.H. Watkins, Learning with delayed rewards, Ph.D. Thesis, Cambridge University, 1989.

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Authors
Sutton, R. S., Precup, D., & Singh, S.
Affiliations
  • AT&T (United States) (US)
  • University of Massachusetts Amherst (US)
Corresponding author
Sutton, Richard S.
Venue
Artificial Intelligence
Publisher
Elsevier BV
Published
1999-08
Volume
112
Issue
1-2
Pages
181-211
ISSN
0004-3702 (ISSN-L)
OpenAlex
W2109910161
Cited by
3,119
References
75
Citations by year
2026: 179 · 2025: 206 · 2024: 188 · 2023: 212 · 2022: 213 · 2021: 270 · 2020: 303 · 2019: 238 · 2018: 183 · 2017: 112 · 2016: 99 · 2015: 80 · 2014: 74 · 2013: 91 · 2012: 86
FWCI
27.32
Type
journal article
Language
en
Open access
Locations
Artificial Intelligence (publishedVersion)
Funders
  • National Science Foundation (9711753, 9511805)
  • Air Force Office of Scientific Research (F49620)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Keywords
temporal abstraction, options framework, semi-Markov decision process, Markov decision process, hierarchical reinforcement learning, Q-learning, dynamic programming

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