Cited by 320Reinforcement Learning in RoboticsAI-based Problem Solving and PlanningArtificial Intelligence in Games

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Recent Advances in Hierarchical Reinforcement Learning

Barto, Andrew G., Mahadevan, Sridhar·2003·Discrete Event Dynamic Systems

DOI 10.1023/a:1025696116075

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Reinforcement Learning: A Survey
Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.301 · Cited by 8,972
Statecharts: a visual formalism for complex systems
Harel, D. (1987) · Science of Computer Programming · doi:10.1016/0167-6423(87)90035-9 · Cited by 6,771
Markov Decision Processes
Puterman, M. L. (1994) · Wiley series in probability and statistics · doi:10.1002/9780470316887 · Cited by 6,057
Planning and acting in partially observable stochastic domains
Kaelbling, L. P., Littman, M. L., & Cassandra, A. R. (1998) · Artificial Intelligence · doi:10.1016/s0004-3702(98)00023-x · Cited by 4,149
Multiagent Systems : A Modern Approach to Distributed Artificial Intelligence
Weiß, G. (2000) · Medical Entomology and Zoology · Cited by 3,458
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Sutton, R. S., Precup, D., & Singh, S. (1999) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00052-1 · Cited by 3,119
Singular Perturbation Methods in Control: Analysis and Design
Kokotović, P. V., Khalil, H. K., & O’Reilly, J. (1999) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611971118 · Cited by 2,504
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
Dietterich, T. G. (2000) · Journal of Artificial Intelligence Research · doi:10.1613/jair.639 · Cited by 1,526
Toward a modern theory of adaptive networks: Expectation and prediction.
Sutton, R. S., & Barto, A. G. (1981) · Psychological Review · doi:10.1037/0033-295x.88.2.135 · Cited by 1,450
Self-citation
Dynamic Programming: Deterministic and Stochastic Models
Bertsekas, D. P. (1987) · Medical Entomology and Zoology · Cited by 1,348

Show all 64 references

Research works printed but not linked: 27

Bertsekas, D. P., and Tsitsiklis, J. N. 1996. Neuro-Dynamic Programming. Belmont, MA: Athena Scientific.
Bradtke, S. J., and Duff. M. O. 1995. Reinforcement learning methods for continuous-time markov decision problems. In G. Tesauro. D. S. Touretzky and T. Leen (eds.). Advances in Neural Information Processing Systems: Proceedings of the 1994 Conference. Cambridge. MA: MIT Press. pp. 393-400.
Crites. R. H. 1996. Large-Scale Dvnaniic Optimization Using Teams of Reinforcement Learning Agents. Ph.D. thesis. Amherst. MA: University of Massachusetts.
Digney, B. 1998. Learning hierarchical control structure from multiple tasks and changing environments. In From Animals to Animals 5: The Fifth Conference on Simulation of Adaptive Behavior. Carnbndge, MA: MIT Press.
Grudic. G. Z., and Ungar. L. H. 2000. Localizing search in reinforcement learning. In Proceedings of the 18th National Conference on Artificial Intelligence, (AAAI-00), pp. 590-595.
Hernandez, N., and Mahadevan. S. 2001. Hierarchical memory-based reinforcement learning. In Advances in Neural information Processing Systems: Proceedings of the 2000 conference. Cambridge, MA: MIT Press, pp. 1047-1053.
Howard, R. A. 1971. Dynamic Probabilistic Systems: Semi-Markar and Decision Processes. New York: Wiley.
Jonsson. A., and Barto. A. G. 2001. Automated state abstraction for options using the li-tree algorithm. In Advances in Neural Information Processing Systems: Proceedings of the 2000 Conference. Cambridge, MA: MIT Press, pp. 1054-1060.
Littman. M. 1994. Markov games as a framework for multi-agent reinforcement learning. In Proceedings of the Eleventh International Conference on Machine Learning. San Francisco, CA: Morgan Kaufmann, pp. 157-163.
McCalIum, A. K. 1996. Reinforcement Learning with Selective Perceptioin and Hidden State. Ph.D. thesis, University of Rochester.
Nourbakhsh, I., Powers, R., and Birchfield, S. 1995. Dervish: An office-navigation robot. Al Magazine 16(2): 53-60.
Precup, D. 2000. Temporal Abstraction in Reinforcentent Learning. Ph.D. thesis, Amherst, MA: University of Massachusetts.
Precup. D., and Sutton, R. S. 1998. Multi-time models for temporally abstract planning. In Advances in Neural information Processing Systems: Proceedings of the 1997 Conference. Cambridge MA: MIT Press, pp. 1050-1056.
Ross. S. 1983. Introduction to Stochastic Dynamic Programming. New York: Academic Press.
Rummery, G. A., and Niranjan, M. 1994. On-line q-learrnng using connectionist systems. Technical Report CUED/F-INFENG/'FR 166, Cambridge University Engineering Department.
Samuel. A. L. 1963. Some studies in machine learning using the game of checkers. IBM Journal on Research and Developntent 3: 211-229, 1959. Reprinted in E. A. Feigenbaurn and J. Feldman (eds.), Computers and Thought. New York: McGraw-Hill, pp. pp71-105.
Shatkay, H., and Kaelbling. L. P. 1997. Learning topological maps with weak local odometric information. In IJC4I (2). 920-929.
Singh. S., and Bertsekas, D. 1997. Reinforcement learning for dynamic channel allocation in cellular telephone systems. In Advances in Neural Information Processing Systems: Proceedings of the 1996 conference. Cambridge, MA: MIT Press, pp. 974-980.
Sutton, R. S. 1996. Generalization in reinforcement leaming: Successful examples using sparse coarse coding. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo (eds.). Advances in Neural Information Processing Svstenis: Proceedings of the 1995 Conference. Cambridge. MA: MIT Press. pp. 1038-1044.
Sutton. R. S., and Barto, A. G. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press.
Tan, M. 1993. Multi-agent reinforcement leaming: Independent vs. cooperative agents. In Proceedings of the Tenth International Conference on Mac/tine Learning. San Francisco, CA: Morgan Kaufmann, pp. 330-337.
Theocharous, G. 2002. Hierarchical Learning and Planning in Partially Observable Markov Derision Processes. Ph.D. Thesis, Michigan State University.
Theocharous, G., and Mahadevan. S. 2002. Approximate planning with hierarchical partially observable markov decision processs for robot navigation. In Proceedings of the IEEE International Conference on Robotics and Autontation (ICRA).
Theocharous, G., Rohanimanesh. K., and Mahadevan, S. 2001. Learning hierarchical partially observable markov decision processs for robot navigation. In Proceedings of the IEEE International Conference o,i Robotics and Automation (ICRA).
Watkins, C. J. C. H. 1989. Learning front Delayed Rewards. Ph.D. thesis, Cambridge, England: Cambridge University.
Watkins, C. J. C. H., and Dayan, P. 1992. Q-learning. Machine Learning 8: 279-292.
Werbos, P. J. 1977. Advanced forecasting methods for global crisis warning and models of intelligence. General Systems Yearbook 22: 25-38.

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Authors: Barto, A. G., & Mahadevan, S.
Affiliations: University of Massachusetts Amherst (US)
ORCID: Barto, Andrew G.
Mahadevan, Sridhar
Venue: Discrete Event Dynamic Systems
Publisher: Springer Science and Business Media LLC
Published: 2003-10
Volume: 13
Issue: 4
Pages: 341-379
ISSN: 0924-6703 (ISSN-L)
1573-7594
DOI: 10.1023/a:1025696116075
OpenAlex: W4249441547
Cited by: 317
References: 64
Citations by year: 2026: 29 · 2025: 44 · 2024: 8 · 2023: 4 · 2022: 10 · 2021: 14 · 2020: 13 · 2019: 20 · 2018: 21 · 2017: 17 · 2016: 11 · 2015: 12 · 2014: 18 · 2013: 20 · 2012: 23
FWCI: 2.49
Type: journal article
Language: en
Open access: closed
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Discrete Event Dynamic Systems (publishedVersion)
Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
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 › Artificial Intelligence in Games
Keywords: hierarchical reinforcement learning, temporal abstraction, semi-Markov decision process, curse of dimensionality, hierarchical control architecture, concurrent actions, multi-agent coordination, hierarchical memory, partial observability

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