Littérature scientifique sur le sujet « Causal reinforcement learning »
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Articles de revues sur le sujet "Causal reinforcement learning"
Madumal, Prashan, Tim Miller, Liz Sonenberg et Frank Vetere. « Explainable Reinforcement Learning through a Causal Lens ». Proceedings of the AAAI Conference on Artificial Intelligence 34, no 03 (3 avril 2020) : 2493–500. http://dx.doi.org/10.1609/aaai.v34i03.5631.
Texte intégralLi, Dezhi, Yunjun Lu, Jianping Wu, Wenlu Zhou et Guangjun Zeng. « Causal Reinforcement Learning for Knowledge Graph Reasoning ». Applied Sciences 14, no 6 (15 mars 2024) : 2498. http://dx.doi.org/10.3390/app14062498.
Texte intégralYang, Dezhi, Guoxian Yu, Jun Wang, Zhengtian Wu et Maozu Guo. « Reinforcement Causal Structure Learning on Order Graph ». Proceedings of the AAAI Conference on Artificial Intelligence 37, no 9 (26 juin 2023) : 10737–44. http://dx.doi.org/10.1609/aaai.v37i9.26274.
Texte intégralMadumal, Prashan. « Explainable Agency in Reinforcement Learning Agents ». Proceedings of the AAAI Conference on Artificial Intelligence 34, no 10 (3 avril 2020) : 13724–25. http://dx.doi.org/10.1609/aaai.v34i10.7134.
Texte intégralHerlau, Tue, et Rasmus Larsen. « Reinforcement Learning of Causal Variables Using Mediation Analysis ». Proceedings of the AAAI Conference on Artificial Intelligence 36, no 6 (28 juin 2022) : 6910–17. http://dx.doi.org/10.1609/aaai.v36i6.20648.
Texte intégralDuong, Tri Dung, Qian Li et Guandong Xu. « Stochastic intervention for causal inference via reinforcement learning ». Neurocomputing 482 (avril 2022) : 40–49. http://dx.doi.org/10.1016/j.neucom.2022.01.086.
Texte intégralZhang, Wei, Xuesong Wang, Haoyu Wang et Yuhu Cheng. « Causal Meta-Reinforcement Learning for Multimodal Remote Sensing Data Classification ». Remote Sensing 16, no 6 (16 mars 2024) : 1055. http://dx.doi.org/10.3390/rs16061055.
Texte intégralVeselic, Sebastijan, Gerhard Jocham, Christian Gausterer, Bernhard Wagner, Miriam Ernhoefer-Reßler, Rupert Lanzenberger, Christoph Eisenegger, Claus Lamm et Annabel Losecaat Vermeer. « A causal role of estradiol in human reinforcement learning ». Hormones and Behavior 134 (août 2021) : 105022. http://dx.doi.org/10.1016/j.yhbeh.2021.105022.
Texte intégralZhou, Zhengyuan, Michael Bloem et Nicholas Bambos. « Infinite Time Horizon Maximum Causal Entropy Inverse Reinforcement Learning ». IEEE Transactions on Automatic Control 63, no 9 (septembre 2018) : 2787–802. http://dx.doi.org/10.1109/tac.2017.2775960.
Texte intégralWang, Zizhao, Caroline Wang, Xuesu Xiao, Yuke Zhu et Peter Stone. « Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning ». Proceedings of the AAAI Conference on Artificial Intelligence 38, no 14 (24 mars 2024) : 15778–86. http://dx.doi.org/10.1609/aaai.v38i14.29507.
Texte intégralThèses sur le sujet "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.
Texte intégralThe 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.
Texte intégralThéro, Héloïse. « Contrôle, agentivité et apprentissage par renforcement ». Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE028/document.
Texte intégralSense 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.
Texte intégralLattimore, Finnian Rachel. « Learning how to act : making good decisions with machine learning ». Phd thesis, 2017. http://hdl.handle.net/1885/144602.
Texte intégralBernigau, 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.
Texte intégralLivres sur le sujet "Causal reinforcement learning"
Chakraborty, Bibhas. Statistical methods for dynamic treatment regimes : Reinforcement learning, causal inference, and personalized medicine. New York, NY : Springer, 2013.
Trouver le texte intégralGershman, Samuel J. Reinforcement Learning and Causal Models. Sous la direction de Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.20.
Texte intégralMoodie, Erica E. M., et Bibhas Chakraborty. Statistical Methods for Dynamic Treatment Regimes : Reinforcement Learning, Causal Inference, and Personalized Medicine. Springer New York, 2015.
Trouver le texte intégralButz, Martin V., et Esther F. Kutter. How the Mind Comes into Being. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.001.0001.
Texte intégralChapitres de livres sur le sujet "Causal reinforcement learning"
Xiong, Momiao. « Reinforcement Learning and Causal Inference ». Dans Artificial Intelligence and Causal Inference, 293–348. Boca Raton : Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003028543-8.
Texte intégralYang, Dezhi, Guoxian Yu, Jun Wang, Zhongmin Yan et Maozu Guo. « Causal Discovery by Graph Attention Reinforcement Learning ». Dans 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.
Texte intégralWeytjens, Hans, Wouter Verbeke et Jochen De Weerdt. « Timed Process Interventions : Causal Inference vs. Reinforcement Learning ». Dans Business Process Management Workshops, 245–58. Cham : Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50974-2_19.
Texte intégralGajcin, Jasmina, et Ivana Dusparic. « ReCCoVER : Detecting Causal Confusion for Explainable Reinforcement Learning ». Dans 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.
Texte intégralFeliciano-Avelino, Ivan, Arquímides Méndez-Molina, Eduardo F. Morales et L. Enrique Sucar. « Causal Based Action Selection Policy for Reinforcement Learning ». Dans Advances in Computational Intelligence, 213–27. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89817-5_16.
Texte intégralPaliwal, Yash, Rajarshi Roy, Jean-Raphaël Gaglione, Nasim Baharisangari, Daniel Neider, Xiaoming Duan, Ufuk Topcu et Zhe Xu. « Reinforcement Learning with Temporal-Logic-Based Causal Diagrams ». Dans Lecture Notes in Computer Science, 123–40. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40837-3_8.
Texte intégralHao, Zhifeng, Haipeng Zhu, Wei Chen et Ruichu Cai. « Latent Causal Dynamics Model for Model-Based Reinforcement Learning ». Dans Neural Information Processing, 219–30. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8082-6_17.
Texte intégralBozorgi, Zahra Dasht, Marlon Dumas, Marcello La Rosa, Artem Polyvyanyy, Mahmoud Shoush et Irene Teinemaa. « Learning When to Treat Business Processes : Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning ». Dans Advanced Information Systems Engineering, 364–80. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34560-9_22.
Texte intégralSridharan, Mohan, et Sarah Rainge. « Integrating Reinforcement Learning and Declarative Programming to Learn Causal Laws in Dynamic Domains ». Dans Social Robotics, 320–29. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11973-1_33.
Texte intégralSwan, Jerry, Eric Nivel, Neel Kant, Jules Hedges, Timothy Atkinson et Bas Steunebrink. « Background ». Dans The Road to General Intelligence, 7–15. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08020-3_2.
Texte intégralActes de conférences sur le sujet "Causal reinforcement learning"
Blübaum, Lukas, et Stefan Heindorf. « Causal Question Answering with Reinforcement Learning ». Dans WWW '24 : The ACM Web Conference 2024. New York, NY, USA : ACM, 2024. http://dx.doi.org/10.1145/3589334.3645610.
Texte intégralZhu, Wenxuan, Chao Yu et Qiang Zhang. « Causal Deep Reinforcement Learning Using Observational Data ». Dans 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.
Texte intégralWang, Xiaoqiang, Yali Du, Shengyu Zhu, Liangjun Ke, Zhitang Chen, Jianye Hao et Jun Wang. « Ordering-Based Causal Discovery with Reinforcement Learning ». Dans 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.
Texte intégralAshton, Hal. « Causal Campbell-Goodhart’s Law and Reinforcement Learning ». Dans 13th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010197300670073.
Texte intégralMa, Hao, Zhiqiang Pu, Yi Pan, Boyin Liu, Junlong Gao et Zhenyu Guo. « Causal Mean Field Multi-Agent Reinforcement Learning ». Dans 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10191654.
Texte intégralYu, Zhongwei, Jingqing Ruan et Dengpeng Xing. « Explainable Reinforcement Learning via a Causal World Model ». Dans 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.
Texte intégralSankar, Namasi G., Ankit Khandelwal et M. Girish Chandra. « Quantum-Enhanced Resilient Reinforcement Learning Using Causal Inference ». Dans 2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS). IEEE, 2024. http://dx.doi.org/10.1109/comsnets59351.2024.10427302.
Texte intégralMéndez-Molina, Arquímides. « Combining Reinforcement Learning and Causal Models for Robotics Applications ». Dans 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.
Texte intégralBloem, Michael, et Nicholas Bambos. « Infinite time horizon maximum causal entropy inverse reinforcement learning ». Dans 2014 IEEE 53rd Annual Conference on Decision and Control (CDC). IEEE, 2014. http://dx.doi.org/10.1109/cdc.2014.7040156.
Texte intégralWang, Siyu, Xiaocong Chen, Dietmar Jannach et Lina Yao. « Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning ». Dans 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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