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Publication search results
found 47 matches
- 2023
- Hao Cheng, Feng Duan, Maochang He:
Spiking Memory Policy with Population-encoding for Partially Observable Markov Decision Process Problems. Cogn. Comput. 15(4): 1153-1166 (2023) - Hao Zhang, Weihua Zhang:
Analytical Solution to a Partially Observable Machine Maintenance Problem with Obvious Failures. Manag. Sci. 69(7): 3993-4015 (2023) - Ugo Rosolia, Yuxiao Chen, Shreyansh Daftry, Masahiro Ono, Yisong Yue, Aaron D. Ames:
The Mixed-Observable Constrained Linear Quadratic Regulator Problem: The Exact Solution and Practical Algorithms. IEEE Trans. Autom. Control. 68(7): 4435-4442 (2023) - Matheus Aparecido do Carmo Alves, Amokh Varma, Yehia Elkhatib, Leandro Soriano Marcolino:
Information-guided Planning: An Online Approach for Partially Observable Problems. NeurIPS 2023 - Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Christos Peridis, Pawel Ladosz, Jeffery Dick, Praveen K. Pilly, Soheil Kolouri:
The configurable tree graph (CT-graph): measurable problems in partially observable and distal reward environments for lifelong reinforcement learning. CoRR abs/2302.10887 (2023) - 2022
- Olivier Tsemogne, Yezekael Hayel, Charles A. Kamhoua, Gabriel Deugoue:
A Partially Observable Stochastic Zero-sum Game for a Network Epidemic Control Problem. Dyn. Games Appl. 12(1): 82-109 (2022) - 2021
- Hyun-Rok Lee, Taesik Lee:
Multi-agent reinforcement learning algorithm to solve a partially-observable multi-agent problem in disaster response. Eur. J. Oper. Res. 291(1): 296-308 (2021) - Xuanchen Xiang, Simon Foo:
Recent Advances in Deep Reinforcement Learning Applications for Solving Partially Observable Markov Decision Processes (POMDP) Problems: Part 1 - Fundamentals and Applications in Games, Robotics and Natural Language Processing. Mach. Learn. Knowl. Extr. 3(3): 554-581 (2021) - Xuanchen Xiang, Simon Y. Foo, Huanyu Zang:
Recent Advances in Deep Reinforcement Learning Applications for Solving Partially Observable Markov Decision Processes (POMDP) Problems Part 2 - Applications in Transportation, Industries, Communications and Networking and More Topics. Mach. Learn. Knowl. Extr. 3(4): 863-878 (2021) - Ngozi Omatu, Joshua L. Phillips:
Benefits of combining dimensional attention and working memory for partially observable reinforcement learning problems. ACM Southeast Conference 2021: 209-213 - Alphonsus Adu-Bredu, Nikhil Devraj, Pin-Han Lin, Zhen Zeng, Odest Chadwicke Jenkins:
Probabilistic Inference in Planning for Partially Observable Long Horizon Problems. IROS 2021: 3154-3161 - Alphonsus Adu-Bredu, Nikhil Devraj, Pin-Han Lin, Zhen Zeng, Odest Chadwicke Jenkins:
Probabilistic Inference in Planning for Partially Observable Long Horizon Problems. CoRR abs/2110.09153 (2021) - 2020
- Caelan Reed Garrett, Chris Paxton, Tomás Lozano-Pérez, Leslie Pack Kaelbling, Dieter Fox:
Online Replanning in Belief Space for Partially Observable Task and Motion Problems. ICRA 2020: 5678-5684 - (Withdrawn) Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer Learning. CoRR abs/2004.06213 (2020)
- 2019
- Caelan Reed Garrett, Chris Paxton, Tomás Lozano-Pérez, Leslie Pack Kaelbling, Dieter Fox:
Online Replanning in Belief Space for Partially Observable Task and Motion Problems. CoRR abs/1911.04577 (2019) - Nibraas Khan, Joshua L. Phillips:
Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems. CoRR abs/1911.10425 (2019) - 2018
- Meiguang He, Quanbo Ge, Weifeng Liu:
Some Problems Analysis of Observability and Observable Degree for Complex Stochastic Systems. ICARM 2018: 863-868 - Luis Hernán García Paucar, Nelly Bencomo:
RE-STORM: mapping the decision-making problem and non-functional requirements trade-off to partially observable markov decision processes. SEAMS@ICSE 2018: 19-25 - Chitradeep G. Hazra:
The Optimization Problem of Quantum Discord In the Language of Correlated Observables. CoRR abs/1809.00156 (2018) - 2017
- Mutlu Cukurova, Rose Luckin, Manolis Mavrikis, Eva Millán:
Machine and Human Observable Differences in Groups' Collaborative Problem-Solving Behaviours. EC-TEL 2017: 17-29 - 2016
- Jicheng Fu, Andres Calderon Jaramillo, Vincent Ng, Farokh B. Bastani, I-Ling Yen:
Fast strong planning for fully observable nondeterministic planning problems. Ann. Math. Artif. Intell. 78(2): 131-155 (2016) - Andrew R. Buck, James M. Keller:
A myopic Monte Carlo strategy for the partially observable travelling salesman problem. CEC 2016: 632-639 - Junyu Zhang:
A special case of partially observable Markov decision processes problem by event-based optimization. ICIT 2016: 1522-1526 - 2015
- Jingnan Fan, Andrzej Ruszczynski:
Dynamic Risk Measures for Finite-State Partially Observable Markov Decision Problems. SIAM Conf. on Control and its Applications 2015: 153-158 - 2014
- Yunlong Liu, Zijiang Yang, Guoli Ji:
Solving partially observable problems with inaccurate PSR models. Inf. Sci. 283: 142-152 (2014) - Hugo Gimbert, Youssouf Oualhadj:
Deciding the Value 1 Problem for $\sharp$ -acyclic Partially Observable Markov Decision Processes. SOFSEM 2014: 281-292 - 2013
- Régis Sabbadin:
A Possibilistic Model for Qualitative Sequential Decision Problems under Uncertainty in Partially Observable Environments. CoRR abs/1301.6736 (2013) - 2012
- Utku Erdogdu:
Efficient partially observable markov decision process based formulation of gene regulatory network control problem (Gen ağlarının kısmi gözlemlenebilir markov karar süreçleri ile modellenerek etkin olarak kontrolü). Middle East Technical University, Turkey, 2012 - Siegmund Duell, Steffen Udluft, Volkmar Sterzing:
Solving Partially Observable Reinforcement Learning Problems with Recurrent Neural Networks. Neural Networks: Tricks of the Trade (2nd ed.) 2012: 709-733 - 2011
- Emma Brunskill, Stuart Russell:
Partially Observable Sequential Decision Making for Problem Selection in an Intelligent Tutoring System. EDM 2011: 327-328
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