Research output per year
Research output per year
Fingerprint
Dive into the research topics where G. Neustroev is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Deep Reinforcement Learning for Active Wake Control
Neustroev, G., Andringa, S. P. E., Verzijlbergh, R. A. & de Weerdt, M. M., 2022, International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), p. 944-953 10 p. (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; vol. 2).Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review
Open AccessFile4 Citations (Scopus)77 Downloads (Pure) -
Generalized Models of Sequential Decision-Making under Uncertainty
Neustroev, G., 2022, 219 p.Research output: Thesis › Dissertation (TU Delft)
Open AccessFile154 Downloads (Pure) -
Generalized Optimistic Q-Learning with Provable Efficiency
Neustroev, G. & de Weerdt, M., May 2020, Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020. An, B., El Fallah Seghrouchni, A. & Sukthankar, G. (eds.). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), p. 913-921 9 p. (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; vol. 2020-May).Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review
Open AccessFile3 Citations (Scopus)133 Downloads (Pure) -
Interval Q-Learning: Balancing Deep and Wide Exploration
Neustroev, G., Ponnambalam, C., de Weerdt, M. & Spaan, M., 2020, Adaptive and Learning Agents Workshop. 7 p.Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review
Open AccessFile140 Downloads (Pure) -
Discovery of Optimal Solution Horizons in Non-Stationary Markov Decision Processes with Unbounded Rewards
Neustroev, G., de Weerdt, M. & Verzijlbergh, R., 2019, Proceedings of the 29th International Conference on Automated Planning and Scheduling, ICAPS 2019. Benton, J., Lipovetzky, N., Onaindia, E., Smith, D. E. & Srivastava, S. (eds.). Association for the Advancement of Artificial Intelligence (AAAI), Vol. 29. p. 292-300 9 p. (Proceedings International Conference on Automated Planning and Scheduling, ICAPS).Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review
Open AccessFile103 Downloads (Pure)
Datasets
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Source code and data for the experiments presented in Deep Reinforcement Learning for Active Wake Control
Neustroev, G. (Creator), de Weerdt, M. M. (Creator), Verzijbergh, R. (Creator) & Andringa, S. P. E. (Creator), TU Delft - 4TU.ResearchData, 4 Feb 2022
DOI: 10.4121/19107257
Dataset/Software: Software
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TulipaClustering.jl
Neustroev, G. (Creator), Tejada-Arango, D. (Creator), Clisby, L. (Creator) & Morales-España, G. (Creator), GitHub/Zenodo, 9 Dec 2023
DOI: 10.5281/zenodo.10419236, https://github.com/TulipaEnergy/TulipaClustering.jl
Dataset/Software: Software
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TulipaEnergyModel.jl
Tejada-Arango, D. (Creator), Morales-España, G. (Creator), Clisby, L. (Creator), Wang, N. (Creator), Soares Siqueira, A. (Creator), Ali, S. (Creator), Soucasse, L. (Creator) & Neustroev, G. (Creator), GitHub/Zenodo, 23 Sept 2023
DOI: 10.5281/zenodo.8363262, https://github.com/TulipaEnergy/TulipaEnergyModel.jl
Dataset/Software: Software
Activities
- 1 Talk or presentation at a conference
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Generalized Optimistic Q-Learning with Provable Efficiency
Greg Neustroev (Speaker)
11 May 2020Activity: Talk or presentation › Talk or presentation at a conference