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Artykuły w czasopismach na temat "Causal reinforcement learning"
Madumal, Prashan, Tim Miller, Liz Sonenberg i Frank Vetere. "Explainable Reinforcement Learning through a Causal Lens". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 03 (3.04.2020): 2493–500. http://dx.doi.org/10.1609/aaai.v34i03.5631.
Pełny tekst źródłaLi, Dezhi, Yunjun Lu, Jianping Wu, Wenlu Zhou i Guangjun Zeng. "Causal Reinforcement Learning for Knowledge Graph Reasoning". Applied Sciences 14, nr 6 (15.03.2024): 2498. http://dx.doi.org/10.3390/app14062498.
Pełny tekst źródłaYang, Dezhi, Guoxian Yu, Jun Wang, Zhengtian Wu i Maozu Guo. "Reinforcement Causal Structure Learning on Order Graph". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 9 (26.06.2023): 10737–44. http://dx.doi.org/10.1609/aaai.v37i9.26274.
Pełny tekst źródłaMadumal, Prashan. "Explainable Agency in Reinforcement Learning Agents". Proceedings of the AAAI Conference on Artificial Intelligence 34, nr 10 (3.04.2020): 13724–25. http://dx.doi.org/10.1609/aaai.v34i10.7134.
Pełny tekst źródłaHerlau, Tue, i Rasmus Larsen. "Reinforcement Learning of Causal Variables Using Mediation Analysis". Proceedings of the AAAI Conference on Artificial Intelligence 36, nr 6 (28.06.2022): 6910–17. http://dx.doi.org/10.1609/aaai.v36i6.20648.
Pełny tekst źródłaDuong, Tri Dung, Qian Li i Guandong Xu. "Stochastic intervention for causal inference via reinforcement learning". Neurocomputing 482 (kwiecień 2022): 40–49. http://dx.doi.org/10.1016/j.neucom.2022.01.086.
Pełny tekst źródłaZhang, Wei, Xuesong Wang, Haoyu Wang i Yuhu Cheng. "Causal Meta-Reinforcement Learning for Multimodal Remote Sensing Data Classification". Remote Sensing 16, nr 6 (16.03.2024): 1055. http://dx.doi.org/10.3390/rs16061055.
Pełny tekst źródłaVeselic, Sebastijan, Gerhard Jocham, Christian Gausterer, Bernhard Wagner, Miriam Ernhoefer-Reßler, Rupert Lanzenberger, Christoph Eisenegger, Claus Lamm i Annabel Losecaat Vermeer. "A causal role of estradiol in human reinforcement learning". Hormones and Behavior 134 (sierpień 2021): 105022. http://dx.doi.org/10.1016/j.yhbeh.2021.105022.
Pełny tekst źródłaZhou, Zhengyuan, Michael Bloem i Nicholas Bambos. "Infinite Time Horizon Maximum Causal Entropy Inverse Reinforcement Learning". IEEE Transactions on Automatic Control 63, nr 9 (wrzesień 2018): 2787–802. http://dx.doi.org/10.1109/tac.2017.2775960.
Pełny tekst źródłaWang, Zizhao, Caroline Wang, Xuesu Xiao, Yuke Zhu i Peter Stone. "Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 14 (24.03.2024): 15778–86. http://dx.doi.org/10.1609/aaai.v38i14.29507.
Pełny tekst źródłaRozprawy doktorskie na temat "Causal reinforcement learning"
Tournaire, Thomas. "Model-based reinforcement learning for dynamic resource allocation in cloud environments". Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS004.
Pełny tekst źródłaThe emergence of new technologies (Internet of Things, smart cities, autonomous vehicles, health, industrial automation, ...) requires efficient resource allocation to satisfy the demand. These new offers are compatible with new 5G network infrastructure since it can provide low latency and reliability. However, these new needs require high computational power to fulfill the demand, implying more energy consumption in particular in cloud infrastructures and more particularly in data centers. Therefore, it is critical to find new solutions that can satisfy these needs still reducing the power usage of resources in cloud environments. In this thesis we propose and compare new AI solutions (Reinforcement Learning) to orchestrate virtual resources in virtual network environments such that performances are guaranteed and operational costs are minimised. We consider queuing systems as a model for clouds IaaS infrastructures and bring learning methodologies to efficiently allocate the right number of resources for the users.Our objective is to minimise a cost function considering performance costs and operational costs. We go through different types of reinforcement learning algorithms (from model-free to relational model-based) to learn the best policy. Reinforcement learning is concerned with how a software agent ought to take actions in an environment to maximise some cumulative reward. We first develop queuing model of a cloud system with one physical node hosting several virtual resources. On this first part we assume the agent perfectly knows the model (dynamics of the environment and the cost function), giving him the opportunity to perform dynamic programming methods for optimal policy computation. Since the model is known in this part, we also concentrate on the properties of the optimal policies, which are threshold-based and hysteresis-based rules. This allows us to integrate the structural property of the policies into MDP algorithms. After providing a concrete cloud model with exponential arrivals with real intensities and energy data for cloud provider, we compare in this first approach efficiency and time computation of MDP algorithms against heuristics built on top of the queuing Markov Chain stationary distributions.In a second part we consider that the agent does not have access to the model of the environment and concentrate our work with reinforcement learning techniques, especially model-based reinforcement learning. We first develop model-based reinforcement learning methods where the agent can re-use its experience replay to update its value function. We also consider MDP online techniques where the autonomous agent approximates environment model to perform dynamic programming. This part is evaluated in a larger network environment with two physical nodes in tandem and we assess convergence time and accuracy of different reinforcement learning methods, mainly model-based techniques versus the state-of-the-art model-free methods (e.g. Q-Learning).The last part focuses on model-based reinforcement learning techniques with relational structure between environment variables. As these tandem networks have structural properties due to their infrastructure shape, we investigate factored and causal approaches built-in reinforcement learning methods to integrate this information. We provide the autonomous agent with a relational knowledge of the environment where it can understand how variables are related to each other. The main goal is to accelerate convergence by: first having a more compact representation with factorisation where we devise a factored MDP online algorithm that we evaluate and compare with model-free and model-based reinforcement learning algorithms; second integrating causal and counterfactual reasoning that can tackle environments with partial observations and unobserved confounders
Bernigau, Holger. "Causal Models over Infinite Graphs and their Application to the Sensorimotor Loop". Doctoral thesis, Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-164734.
Pełny tekst źródłaThéro, Héloïse. "Contrôle, agentivité et apprentissage par renforcement". Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE028/document.
Pełny tekst źródłaSense of agency or subjective control can be defined by the feeling that we control our actions, and through them effects in the outside world. This cluster of experiences depend on the ability to learn action-outcome contingencies and a more classical algorithm to model this originates in the field of human reinforcementlearning. In this PhD thesis, we used the cognitive modeling approach to investigate further the interaction between perceived control and reinforcement learning. First, we saw that participants undergoing a reinforcement-learning task experienced higher agency; this influence of reinforcement learning on agency comes as no surprise, because reinforcement learning relies on linking a voluntary action and its outcome. But our results also suggest that agency influences reinforcement learning in two ways. We found that people learn actionoutcome contingencies based on a default assumption: their actions make a difference to the world. Finally, we also found that the mere fact of choosing freely shapes the learning processes following that decision. Our general conclusion is that agency and reinforcement learning, two fundamental fields of human psychology, are deeply intertwined. Contrary to machines, humans do care about being in control, or about making the right choice, and this results in integrating information in a one-sided way
Jonsson, Anders. "A causal approach to hierarchical decomposition in reinforcement learning". 2006. https://scholarworks.umass.edu/dissertations/AAI3212735.
Pełny tekst źródłaLattimore, Finnian Rachel. "Learning how to act: making good decisions with machine learning". Phd thesis, 2017. http://hdl.handle.net/1885/144602.
Pełny tekst źródłaBernigau, Holger. "Causal Models over Infinite Graphs and their Application to the Sensorimotor Loop: Causal Models over Infinite Graphs and their Application to theSensorimotor Loop: General Stochastic Aspects and GradientMethods for Optimal Control". Doctoral thesis, 2014. https://ul.qucosa.de/id/qucosa%3A13254.
Pełny tekst źródłaKsiążki na temat "Causal reinforcement learning"
Chakraborty, Bibhas. Statistical methods for dynamic treatment regimes: Reinforcement learning, causal inference, and personalized medicine. New York, NY: Springer, 2013.
Znajdź pełny tekst źródłaGershman, Samuel J. Reinforcement Learning and Causal Models. Redaktor Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.20.
Pełny tekst źródłaMoodie, Erica E. M., i Bibhas Chakraborty. Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine. Springer New York, 2015.
Znajdź pełny tekst źródłaButz, Martin V., i Esther F. Kutter. How the Mind Comes into Being. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.001.0001.
Pełny tekst źródłaCzęści książek na temat "Causal reinforcement learning"
Xiong, Momiao. "Reinforcement Learning and Causal Inference". W Artificial Intelligence and Causal Inference, 293–348. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003028543-8.
Pełny tekst źródłaYang, Dezhi, Guoxian Yu, Jun Wang, Zhongmin Yan i Maozu Guo. "Causal Discovery by Graph Attention Reinforcement Learning". W Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), 28–36. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2023. http://dx.doi.org/10.1137/1.9781611977653.ch4.
Pełny tekst źródłaWeytjens, Hans, Wouter Verbeke i Jochen De Weerdt. "Timed Process Interventions: Causal Inference vs. Reinforcement Learning". W Business Process Management Workshops, 245–58. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50974-2_19.
Pełny tekst źródłaGajcin, Jasmina, i Ivana Dusparic. "ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement Learning". W Explainable and Transparent AI and Multi-Agent Systems, 38–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15565-9_3.
Pełny tekst źródłaFeliciano-Avelino, Ivan, Arquímides Méndez-Molina, Eduardo F. Morales i L. Enrique Sucar. "Causal Based Action Selection Policy for Reinforcement Learning". W Advances in Computational Intelligence, 213–27. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89817-5_16.
Pełny tekst źródłaPaliwal, Yash, Rajarshi Roy, Jean-Raphaël Gaglione, Nasim Baharisangari, Daniel Neider, Xiaoming Duan, Ufuk Topcu i Zhe Xu. "Reinforcement Learning with Temporal-Logic-Based Causal Diagrams". W Lecture Notes in Computer Science, 123–40. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40837-3_8.
Pełny tekst źródłaHao, Zhifeng, Haipeng Zhu, Wei Chen i Ruichu Cai. "Latent Causal Dynamics Model for Model-Based Reinforcement Learning". W Neural Information Processing, 219–30. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8082-6_17.
Pełny tekst źródłaBozorgi, Zahra Dasht, Marlon Dumas, Marcello La Rosa, Artem Polyvyanyy, Mahmoud Shoush i Irene Teinemaa. "Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning". W Advanced Information Systems Engineering, 364–80. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34560-9_22.
Pełny tekst źródłaSridharan, Mohan, i Sarah Rainge. "Integrating Reinforcement Learning and Declarative Programming to Learn Causal Laws in Dynamic Domains". W Social Robotics, 320–29. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11973-1_33.
Pełny tekst źródłaSwan, Jerry, Eric Nivel, Neel Kant, Jules Hedges, Timothy Atkinson i Bas Steunebrink. "Background". W The Road to General Intelligence, 7–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08020-3_2.
Pełny tekst źródłaStreszczenia konferencji na temat "Causal reinforcement learning"
Blübaum, Lukas, i Stefan Heindorf. "Causal Question Answering with Reinforcement Learning". W WWW '24: The ACM Web Conference 2024. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3589334.3645610.
Pełny tekst źródłaZhu, Wenxuan, Chao Yu i Qiang Zhang. "Causal Deep Reinforcement Learning Using Observational Data". W Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/524.
Pełny tekst źródłaWang, Xiaoqiang, Yali Du, Shengyu Zhu, Liangjun Ke, Zhitang Chen, Jianye Hao i Jun Wang. "Ordering-Based Causal Discovery with Reinforcement Learning". W Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/491.
Pełny tekst źródłaAshton, Hal. "Causal Campbell-Goodhart’s Law and Reinforcement Learning". W 13th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010197300670073.
Pełny tekst źródłaMa, Hao, Zhiqiang Pu, Yi Pan, Boyin Liu, Junlong Gao i Zhenyu Guo. "Causal Mean Field Multi-Agent Reinforcement Learning". W 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10191654.
Pełny tekst źródłaYu, Zhongwei, Jingqing Ruan i Dengpeng Xing. "Explainable Reinforcement Learning via a Causal World Model". W Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/505.
Pełny tekst źródłaSankar, Namasi G., Ankit Khandelwal i M. Girish Chandra. "Quantum-Enhanced Resilient Reinforcement Learning Using Causal Inference". W 2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS). IEEE, 2024. http://dx.doi.org/10.1109/comsnets59351.2024.10427302.
Pełny tekst źródłaMéndez-Molina, Arquímides. "Combining Reinforcement Learning and Causal Models for Robotics Applications". W Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/684.
Pełny tekst źródłaBloem, Michael, i Nicholas Bambos. "Infinite time horizon maximum causal entropy inverse reinforcement learning". W 2014 IEEE 53rd Annual Conference on Decision and Control (CDC). IEEE, 2014. http://dx.doi.org/10.1109/cdc.2014.7040156.
Pełny tekst źródłaWang, Siyu, Xiaocong Chen, Dietmar Jannach i Lina Yao. "Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning". W SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3539618.3591648.
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