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Relevant bibliographies by topics / Causal reinforcement learning

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Academic literature on the topic 'Causal reinforcement learning'

Author: Grafiati

Published: 1 June 2024

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Journal articles on the topic "Causal reinforcement learning"

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Madumal, Prashan, Tim Miller, Liz Sonenberg, and Frank Vetere. "Explainable Reinforcement Learning through a Causal Lens." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 03 (2020): 2493–500. http://dx.doi.org/10.1609/aaai.v34i03.5631.

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Prominent theories in cognitive science propose that humans understand and represent the knowledge of the world through causal relationships. In making sense of the world, we build causal models in our mind to encode cause-effect relations of events and use these to explain why new events happen by referring to counterfactuals — things that did not happen. In this paper, we use causal models to derive causal explanations of the behaviour of model-free reinforcement learning agents. We present an approach that learns a structural causal model during reinforcement learning and encodes causal rel

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Li, Dezhi, Yunjun Lu, Jianping Wu, Wenlu Zhou, and Guangjun Zeng. "Causal Reinforcement Learning for Knowledge Graph Reasoning." Applied Sciences 14, no. 6 (2024): 2498. http://dx.doi.org/10.3390/app14062498.

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Knowledge graph reasoning can deduce new facts and relationships, which is an important research direction of knowledge graphs. Most of the existing methods are based on end-to-end reasoning which cannot effectively use the knowledge graph, so consequently the performance of the method still needs to be improved. Therefore, we combine causal inference with reinforcement learning and propose a new framework for knowledge graph reasoning. By combining the counterfactual method in causal inference, our method can obtain more information as prior knowledge and integrate it into the control strateg

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Yang, Dezhi, Guoxian Yu, Jun Wang, Zhengtian Wu, and Maozu Guo. "Reinforcement Causal Structure Learning on Order Graph." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10737–44. http://dx.doi.org/10.1609/aaai.v37i9.26274.

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Learning directed acyclic graph (DAG) that describes the causality of observed data is a very challenging but important task. Due to the limited quantity and quality of observed data, and non-identifiability of causal graph, it is almost impossible to infer a single precise DAG. Some methods approximate the posterior distribution of DAGs to explore the DAG space via Markov chain Monte Carlo (MCMC), but the DAG space is over the nature of super-exponential growth, accurately characterizing the whole distribution over DAGs is very intractable. In this paper, we propose Reinforcement Causal Struc

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Madumal, Prashan. "Explainable Agency in Reinforcement Learning Agents." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13724–25. http://dx.doi.org/10.1609/aaai.v34i10.7134.

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This thesis explores how reinforcement learning (RL) agents can provide explanations for their actions and behaviours. As humans, we build causal models to encode cause-effect relations of events and use these to explain why events happen. Taking inspiration from cognitive psychology and social science literature, I build causal explanation models and explanation dialogue models for RL agents. By mimicking human-like explanation models, these agents can provide explanations that are natural and intuitive to humans.

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Herlau, Tue, and Rasmus Larsen. "Reinforcement Learning of Causal Variables Using Mediation Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6910–17. http://dx.doi.org/10.1609/aaai.v36i6.20648.

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We consider the problem of acquiring causal representations and concepts in a reinforcement learning setting. Our approach defines a causal variable as being both manipulable by a policy, and able to predict the outcome. We thereby obtain a parsimonious causal graph in which interventions occur at the level of policies. The approach avoids defining a generative model of the data, prior pre-processing, or learning the transition kernel of the Markov decision process. Instead, causal variables and policies are determined by maximizing a new optimization target inspired by mediation analysis, whi

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Duong, Tri Dung, Qian Li, and Guandong Xu. "Stochastic intervention for causal inference via reinforcement learning." Neurocomputing 482 (April 2022): 40–49. http://dx.doi.org/10.1016/j.neucom.2022.01.086.

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Zhang, Wei, Xuesong Wang, Haoyu Wang, and Yuhu Cheng. "Causal Meta-Reinforcement Learning for Multimodal Remote Sensing Data Classification." Remote Sensing 16, no. 6 (2024): 1055. http://dx.doi.org/10.3390/rs16061055.

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Multimodal remote sensing data classification can enhance a model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks has become the focus of researchers. Inspired by the human feedback learning mechanism, causal reasoning mechanism, and knowledge induction mechanism, this paper integrates causal learning, reinforcement learning, and meta learning into a unified remote sensing data classification framework and proposes causal meta-reinforcement learning (CMRL). First, bas

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Veselic, Sebastijan, Gerhard Jocham, Christian Gausterer, et al. "A causal role of estradiol in human reinforcement learning." Hormones and Behavior 134 (August 2021): 105022. http://dx.doi.org/10.1016/j.yhbeh.2021.105022.

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Zhou, Zhengyuan, Michael Bloem, and Nicholas Bambos. "Infinite Time Horizon Maximum Causal Entropy Inverse Reinforcement Learning." IEEE Transactions on Automatic Control 63, no. 9 (2018): 2787–802. http://dx.doi.org/10.1109/tac.2017.2775960.

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Wang, Zizhao, Caroline Wang, Xuesu Xiao, Yuke Zhu, and Peter Stone. "Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (2024): 15778–86. http://dx.doi.org/10.1609/aaai.v38i14.29507.

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Two desiderata of reinforcement learning (RL) algorithms are the ability to learn from relatively little experience and the ability to learn policies that generalize to a range of problem specifications. In factored state spaces, one approach towards achieving both goals is to learn state abstractions, which only keep the necessary variables for learning the tasks at hand. This paper introduces Causal Bisimulation Modeling (CBM), a method that learns the causal relationships in the dynamics and reward functions for each task to derive a minimal, task-specific abstraction. CBM leverages and imp

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Dissertations / Theses on the topic "Causal reinforcement learning"

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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.

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L'émergence de nouvelles technologies nécessite une allocation efficace des ressources pour satisfaire la demande. Cependant, ces nouveaux besoins nécessitent une puissance de calcul élevée impliquant une plus grande consommation d'énergie notamment dans les infrastructures cloud et data centers. Il est donc essentiel de trouver de nouvelles solutions qui peuvent satisfaire ces besoins tout en réduisant la consommation d'énergie des ressources. Dans cette thèse, nous proposons et comparons de nouvelles solutions d'IA (apprentissage par renforcement RL) pour orchestrer les ressources virtuelles

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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.

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Motivation and background The enormous amount of capabilities that every human learns throughout his life, is probably among the most remarkable and fascinating aspects of life. Learning has therefore drawn lots of interest from scientists working in very different fields like philosophy, biology, sociology, educational sciences, computer sciences and mathematics. This thesis focuses on the information theoretical and mathematical aspects of learning. We are interested in the learning process of an agent (which can be for example a human, an animal, a robot, an economical institution or a s

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Théro, Héloïse. "Contrôle, agentivité et apprentissage par renforcement." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE028/document.

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Le sentiment d’agentivité est défini comme le sentiment de contrôler nos actions, et à travers elles, les évènements du monde extérieur. Cet ensemble phénoménologique dépend de notre capacité d’apprendre les contingences entre nos actions et leurs résultats, et un algorithme classique pour modéliser cela vient du domaine de l’apprentissage par renforcement. Dans cette thèse, nous avons utilisé l’approche de modélisation cognitive pour étudier l’interaction entre agentivité et apprentissage par renforcement. Tout d’abord, les participants réalisant une tâche d’apprentissage par renforcement ten

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Jonsson, Anders. "A causal approach to hierarchical decomposition in reinforcement learning." 2006. https://scholarworks.umass.edu/dissertations/AAI3212735.

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Reinforcement learning provides a means for autonomous agents to improve their action selection strategies without the need for explicit training information provided by an informed instructor. Theoretical and empirical results indicate that reinforcement learning algorithms can efficiently determine optimal or approximately optimal policies in tasks of limited size. However, as the size of a task grows, reinforcement learning algorithms become less consistent and less efficient at determining a useful policy. A key challenge in reinforcement learning is to develop methods that facilitate scal

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Lattimore, Finnian Rachel. "Learning how to act: making good decisions with machine learning." Phd thesis, 2017. http://hdl.handle.net/1885/144602.

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This thesis is about machine learning and statistical approaches to decision making. How can we learn from data to anticipate the consequence of, and optimally select, interventions or actions? Problems such as deciding which medication to prescribe to patients, who should be released on bail, and how much to charge for insurance are ubiquitous, and have far reaching impacts on our lives. There are two fundamental approaches to learning how to act: reinforcement learning, in which an agent directly intervenes in a system and learns from the outcome, and

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Bernigau, 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.

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Motivation and background The enormous amount of capabilities that every human learns throughout his life, is probably among the most remarkable and fascinating aspects of life. Learning has therefore drawn lots of interest from scientists working in very different fields like philosophy, biology, sociology, educational sciences, computer sciences and mathematics. This thesis focuses on the information theoretical and mathematical aspects of learning. We are interested in the learning process of an agent (which can be for example a human, an animal, a robot, an economical institution or a s

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Books on the topic "Causal reinforcement learning"

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Chakraborty, Bibhas. Statistical methods for dynamic treatment regimes: Reinforcement learning, causal inference, and personalized medicine. Springer, 2013.

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Gershman, Samuel J. Reinforcement Learning and Causal Models. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.20.

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This chapter reviews the diverse roles that causal knowledge plays in reinforcement learning. The first half of the chapter contrasts a “model-free” system that learns to repeat actions that lead to reward with a “model-based” system that learns a probabilistic causal model of the environment, which it then uses to plan action sequences. Evidence suggests that these two systems coexist in the brain, both competing and cooperating with each other. The interplay of two systems allows the brain to negotiate a balance between cognitively cheap but inaccurate model-free algorithms and accurate but

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Moodie, Erica E. M., and Bibhas Chakraborty. Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine. Springer New York, 2015.

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Butz, Martin V., and Esther F. Kutter. How the Mind Comes into Being. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.001.0001.

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For more than 2000 years Greek philosophers have thought about the puzzling introspectively assessed dichotomy between our physical bodies and our seemingly non-physical minds. How is it that we can think highly abstract thoughts, seemingly fully detached from actual, physical reality? Despite the obvious interactions between mind and body (we get tired, we are hungry, we stay up late despite being tired, etc.), until today it remains puzzling how our mind controls our body, and vice versa, how our body shapes our mind. Despite a big movement towards embodied cognitive science over the last 20

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Book chapters on the topic "Causal reinforcement learning"

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Xiong, Momiao. "Reinforcement Learning and Causal Inference." In Artificial Intelligence and Causal Inference. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003028543-8.

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Yang, Dezhi, Guoxian Yu, Jun Wang, Zhongmin Yan, and Maozu Guo. "Causal Discovery by Graph Attention Reinforcement Learning." In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics, 2023. http://dx.doi.org/10.1137/1.9781611977653.ch4.

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Weytjens, Hans, Wouter Verbeke, and Jochen De Weerdt. "Timed Process Interventions: Causal Inference vs. Reinforcement Learning." In Business Process Management Workshops. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50974-2_19.

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Gajcin, Jasmina, and Ivana Dusparic. "ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement Learning." In Explainable and Transparent AI and Multi-Agent Systems. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15565-9_3.

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Feliciano-Avelino, Ivan, Arquímides Méndez-Molina, Eduardo F. Morales, and L. Enrique Sucar. "Causal Based Action Selection Policy for Reinforcement Learning." In Advances in Computational Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89817-5_16.

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Paliwal, Yash, Rajarshi Roy, Jean-Raphaël Gaglione, et al. "Reinforcement Learning with Temporal-Logic-Based Causal Diagrams." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40837-3_8.

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Hao, Zhifeng, Haipeng Zhu, Wei Chen, and Ruichu Cai. "Latent Causal Dynamics Model for Model-Based Reinforcement Learning." In Neural Information Processing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8082-6_17.

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Bozorgi, Zahra Dasht, Marlon Dumas, Marcello La Rosa, Artem Polyvyanyy, Mahmoud Shoush, and Irene Teinemaa. "Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning." In Advanced Information Systems Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34560-9_22.

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AbstractIncreasing the success rate of a process, i.e. the percentage of cases that end in a positive outcome, is a recurrent process improvement goal. At runtime, there are often certain actions (a.k.a. treatments) that workers may execute to lift the probability that a case ends in a positive outcome. For example, in a loan origination process, a possible treatment is to issue multiple loan offers to increase the probability that the customer takes a loan. Each treatment has a cost. Thus, when defining policies for prescribing treatments to cases, managers need to consider the net gain of the treatments. Also, the effect of a treatment varies over time: treating a case earlier may be more effective than later in a case. This paper presents a prescriptive monitoring method that automates this decision-making task. The method combines causal inference and reinforcement learning to learn treatment policies that maximize the net gain. The method leverages a conformal prediction technique to speed up the convergence of the reinforcement learning mechanism by separating cases that are likely to end up in a positive or negative outcome, from uncertain cases. An evaluation on two real-life datasets shows that the proposed method outperforms a state-of-the-art baseline.

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Sridharan, Mohan, and Sarah Rainge. "Integrating Reinforcement Learning and Declarative Programming to Learn Causal Laws in Dynamic Domains." In Social Robotics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11973-1_33.

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Swan, Jerry, Eric Nivel, Neel Kant, Jules Hedges, Timothy Atkinson, and Bas Steunebrink. "Background." In The Road to General Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08020-3_2.

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AbstractRecent years have seen an explosion in academic, industrial, and popular interest in AI, as exemplified by machine learning and primarily driven by the widely-reported successes of deep- and reinforcement learning (e.g. [314, 315, 351]). Deep learning is essentially predicated on the notion that, with a sufficiently large training set, the statistical correlations captured by training will actually be causal [310]. However, in the absence of convergence theorems to support this, it remains a hypothesis. Indeed, insofar as there is evidence, it increasingly indicates to the contrary, since the application of enormous volumes of computational effort has still failed to deliver models with the generalization capability of an infant. There is accordingly increasing discussion about what further conceptual or practical insights might be required [57]. At the time of writing, the very definition of deep learning is in flux, with one Turing Award laureate defining it as “a way to try to make machines intelligent by allowing computers to learn from examples” and another as “differentiable programming”. We argue in the following that deep learning is highly unlikely to yield intelligence, at the very least while it equates intelligence with “solving a regression problem”.

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Conference papers on the topic "Causal reinforcement learning"

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Blübaum, Lukas, and Stefan Heindorf. "Causal Question Answering with Reinforcement Learning." In WWW '24: The ACM Web Conference 2024. ACM, 2024. http://dx.doi.org/10.1145/3589334.3645610.

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Zhu, Wenxuan, Chao Yu, and Qiang Zhang. "Causal Deep Reinforcement Learning Using Observational Data." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/524.

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Deep reinforcement learning (DRL) requires the collection of interventional data, which is sometimes expensive and even unethical in the real world, such as in the autonomous driving and the medical field. Offline reinforcement learning promises to alleviate this issue by exploiting the vast amount of observational data available in the real world. However, observational data may mislead the learning agent to undesirable outcomes if the behavior policy that generates the data depends on unobserved random variables (i.e., confounders). In this paper, we propose two deconfounding methods in DRL

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Wang, Xiaoqiang, Yali Du, Shengyu Zhu, et al. "Ordering-Based Causal Discovery with Reinforcement Learning." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/491.

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It is a long-standing question to discover causal relations among a set of variables in many empirical sciences. Recently, Reinforcement Learning (RL) has achieved promising results in causal discovery from observational data. However, searching the space of directed graphs and enforcing acyclicity by implicit penalties tend to be inefficient and restrict the existing RL-based method to small scale problems. In this work, we propose a novel RL-based approach for causal discovery, by incorporating RL into the ordering-based paradigm. Specifically, we formulate the ordering search problem as a m

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Ashton, Hal. "Causal Campbell-Goodhart’s Law and Reinforcement Learning." In 13th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010197300670073.

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Ma, Hao, Zhiqiang Pu, Yi Pan, Boyin Liu, Junlong Gao, and Zhenyu Guo. "Causal Mean Field Multi-Agent Reinforcement Learning." In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10191654.

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Yu, Zhongwei, Jingqing Ruan, and Dengpeng Xing. "Explainable Reinforcement Learning via a Causal World Model." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/505.

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Generating explanations for reinforcement learning (RL) is challenging as actions may produce long-term effects on the future. In this paper, we develop a novel framework for explainable RL by learning a causal world model without prior knowledge of the causal structure of the environment. The model captures the influence of actions, allowing us to interpret the long-term effects of actions through causal chains, which present how actions influence environmental variables and finally lead to rewards. Different from most explanatory models which suffer from low accuracy, our model remains accur

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Sankar, Namasi G., Ankit Khandelwal, and M. Girish Chandra. "Quantum-Enhanced Resilient Reinforcement Learning Using Causal Inference." In 2024 16th International Conference on COMmunication Systems & NETworkS (COMSNETS). IEEE, 2024. http://dx.doi.org/10.1109/comsnets59351.2024.10427302.

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Méndez-Molina, Arquímides. "Combining Reinforcement Learning and Causal Models for Robotics Applications." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/684.

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The relation between Reinforcement learning (RL) and Causal Modeling(CM) is an underexplored area with untapped potential for any learning task. In this extended abstract of our Ph.D. research proposal, we present a way to combine both areas to improve their respective learning processes, especially in the context of our application area (service robotics). The preliminary results obtained so far are a good starting point for thinking about the success of our research project.

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Bloem, Michael, and Nicholas Bambos. "Infinite time horizon maximum causal entropy inverse reinforcement learning." In 2014 IEEE 53rd Annual Conference on Decision and Control (CDC). IEEE, 2014. http://dx.doi.org/10.1109/cdc.2014.7040156.

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Wang, Siyu, Xiaocong Chen, Dietmar Jannach, and Lina Yao. "Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning." In SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2023. http://dx.doi.org/10.1145/3539618.3591648.

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