Cumincad : CUMINCAD References : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

PDF papers
References

Hits 101 to 120 of 7341

Reformat results as: short short into frame detailed detailed into frame

Details	Citation	Select
	Akizuki, Y., Bernhard, M., Kakooee, R., Kladeftira, M., & Dillenburger, B. (2020) Generative modelling with design constraints: Reinforcement learning for object generation , RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th International Conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2020, 1

	Apolinarska, A. A., Pacher, M., Li, H., Cote, N., Pastrana, R., Gramazio, F. & Kohler, M. (2021) Robotic assembly of timber joints using reinforcement learning , Automation in Construction, 103569. https://doi.org/10.1016/j.autcon.2021.103569Deplazes, A. (2000, January). Diskussion: Holz: indifferent, synthetisch - DETAIL inspiration. https://inspiration.detail.de/diskussion-holz-indifferent-synthetisch-107263.htmlFleischmann, P., Casas, G., & Lyrenmann, M. (2020). COMPAS RRC: Online control for ABB robots over a simple-to-use Python interface. Retrieved from https://doi.org/10.5281/zenodo.4639418Herzog, T., Natterer, J., Schweizer, R., Volz, M., Hauser, G., Winter, W., Zeitler, F., Draeger, K., & Institut für Internationale Architektur-Dokumentation. (2003). Holzbau Atlas. Birkhäuser

	Apolinarska, AA, Pacher, M, Li, H, Cote, N, Pastrana, R, Gramazio, F and Kohler, M (2020) Robotic Assembly of Timber Joints using Reinforcement Learning , Automation in Construction, p. in press

	Apolinarska, AA, Pacher, M, Li, H, Cote, N, Pastrana, R, Gramazio, F and Kohler, M (2021) Robotic assembly of timber joints using reinforcement learning , Automation in Construction, 125, p. 103569

	Bakker, B., Schmidhuber, J. (2004) Hierarchical Reinforcement Learning Based on Subgoal Discovery and Subpolicy Specialization , Proceedings of the 8th Conference on Intelligent Autonomous Systems, Amsterdam, 2004, 438-445

	Barto, A and Sutton, R (1992) Reinforcement Learning: An Introduction , The MIT Press, London

	Bello, I, Pham, H, Le, QV, Norouzi, M and Bengio, S (2016) Neural combinatorial optimization with reinforcement learning , arXiv preprint arXiv:1611.09940, 1611, pp. 1-15

	Chang, S., N. Saha, D. Castro-Lacouture, and P. P. J. Yang (2019) Multivariate Relationships between Campus Design Parameters and Energy Performance Using Reinforcement Learning and Parametric Modeling , Applied Energy 249: 253–264

	Chang, S., Saha, N., Castro-Lacouture, D. & Yang, P. P. J. (2019) Multivariate relationships between campus design parameters and energy performance using reinforcement learning and parametric modeling , Applied energy, 249, 253-264

	Chhabra JPS and Warn GP (2019) A method for model selection using reinforcement learning when viewing design as a sequential decision process , Struct Multidiscip Optim 2019; 59: 1521–1542.

	Cobbe, K, Klimov, O, Hesse, C, Kim, T and Schulman, J (2019) Quantifying Generalization in Reinforcement Learning , arXiv:1812.02341 [cs, stat], /, p. /

	Crites, R.H., & Barto, A.G. (1996) Improving elevator performance using reinforcement learning , Advances in Neural Information Processing Systems, vol. 8

	Crites, R.H., and Barto, A.G. (1996) Improving elevator performance using reinforcement learning , Touretzky, D.S., Mozer, M.C., and Hasselmo, M.E., eds., Advances in Neural Information Processing Systems, 1996, 8, 1017–1023

	Cutler, M., T.J. Walsh, and J.P. How (2015) Real-world reinforcement learning via multifidelity simulators , IEEE Transactions on Robotics 31 (3): 655–671

	Dalamagkidis, K., D. Kolokotsa, K. Kalaitzakis, and G.S. Stavrakakis (2007) Reinforcement learning for energy conservation and comfort in buildings , Building and Environment 42 (7): 2686–2698

	Dowling, J., Curran, E., Cunningham, R., and Cahill, V. (2005) Using Feedback in Collaborative Reinforcement Learning to Adapt and Optimise Decentralised Distributed Systems , IEEE Transactions on Systems, Man and Cybernetics (Part A), Special Issue on Engineering Self-Orangized Distributed Systems, 35, no. 3

	Ernst, D., M. Glavic, F. Capitanescu, and L. Wehenkel (2009) Reinforcement learning versus model predictive control: A comparison on a power system problem , IEEE Transactions on Systems, Man, and Cybernetics, PartB (Cybernetics) 39 (2): 517–529

	Fang, Zhihao (2020) Towards Multi-Drone Autonomous Construction via Deep Reinforcement Learning , Master’s thesis. Carnegie Mellon University

	Gal, O., & Doytsher, Y. (2018) Visible routes in 3D dense city using reinforcement learning , International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(4/W10), 41-46. https://doi.org/10.5194/isprs-archives-XLII-4-W10-41-2018

	Garnier, Paul, Viquerat, Jonathan, Rabault, Jean, Larcher, Aurélien, Kuhnle, Alexander and Hachem, Elie (2019) A review on deep reinforcement learning for fluid mechanics , arXiv preprint arXiv:1908.04127, ., p. .

For more results click below: