Causal reinforcement learning ijcai. Abstract: We present a .

Causal reinforcement learning ijcai. Yan Zeng, Ruichu Cai, Fuchun Sun, Libo Huang, Zhifeng Hao. A survey on causal reinforcement learning. #tutorial Towards a new wave of reinforcement learning at #T28: Towards Causal Reinforcement Learning: Empowering Agents with Causality by Zhihong Deng, Jing Jiang, Chengqi Zhang Abstract: While Cooperative Multi-Agent Reinforcement Learning (MARL) necessitates seamless collaboration among agents, often represented by an underlying relation graph. Recent works have proposed many different types of fairness notions, but how unfairness arises in RL problems remains unclear. See full list on crl. This chapter will illustrate where causality fits into RL settings, what causal RL techniques exist, and the challenges that come with combining causal methods and reinforcement learning. -H. Feb 18, 2025 · Causal learning helps provide a straightforward and effective explanation. Electronic proceedings of IJCAI 2022Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method Qize Jiang, Minhao Qin, Shengmin Shi, Weiwei Sun, Baihua Zheng May 14, 2021 · 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 In sequential decision-making problems involving sensitive attributes like race and gender, reinforcement learning (RL) agents must carefully consider long-term fairness while maximizing returns. IJCAI International Joint Conference on Artificial Intelligence. 🔍 In our research, we tackle the challenge of fairness in Aug 14, 2025 · Pre-conference Tutorials: Traditional Approaches to Knowledge Management; Various learning methodologies for classification; Reinforcement Learning: From Foundations to Advanced Techniques 19th IJCAI-05, July 30 – August 5, 2005, Edinburgh, Scotland, UK Aug 1, 2023 · Causal reinforcement learning using observational and interventional data. Abstract: We present a title = {Ordering-Based Causal Discovery with Reinforcement Learning}, author = {Wang, Xiaoqiang and Du, Yali and Zhu, Shengyu and Ke, Liangjun and Chen, Zhitang and Hao, Jianye and Wang, Jun}, booktitle = {Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, {IJCAI-21}}, It is a long-standing question to discover causal rela- tions among a set of variables in many empirical sci- ences. cn As multi-agent reinforcement learning (MARL) systems are increasingly deployed throughout society, it is imperative yet challenging for users to understand the emergent behaviors of MARL agents in complex environments. A Bi-Level Meta-Optimization Approach. In this paper, we develop a novel framework for explainable RL by learning a causal worl Sep 15, 2025 · Existing LLM alignment methods such as reinforcement learning from human feedback (RLHF) alleviate biases primarily based on reward signals from current model outputs without considering the source of biases. Abstract Reinforcement Learning (RL) has shown great promise in domains like healthcare and robotics but often struggles with adoption due to its lack of in-terpretability. Causal Data Science; Causal Fairness Analysis; Causal Reinforcement Learning. Moreover, we develop a reward model rehearsal technique to recover the reward signals for previous objectives, thus alleviating catastrophic forgetting. Contribute to sanghack81/2019-IJCAI-CRL development by creating an account on GitHub. Sep 28, 2020 · It is a long-standing question to discover causal relations among a set of variables in many empirical sciences. In this extended abstract of our Ph. However, observational data may mislead the This work is based on a framework called causal reinforcement learning, which was introduced in a tutorial at ICML-20 and summarized in this paper (link). edu. causalai. In this paper, we address this gap in the literature by investigating the Causal Reinforcement Learning (CRL) is a suite of algorithms, embedding causal knowledge into RL for more efficient and effective model learning, policy evaluation, or policy optimization. TNNLS 2024 Xuexin Chen, Ruichu Cai*, Zhengting Huang, Yuxuan Zhu, Julien Horwood, Zhifeng Hao, Zijian Li, Jose Miguel Hernandez-Lobato. , 2022]. However, these methods are still incapable of being generalizable enough to solve new tasks at a low cost. To answer these questions, we import ideas from the well-established causal framework of do-calculus, and we express model-based reinforcement learning as a causal inference problem. Our tutorial delves into this fascinating research area, offering participants a unique opportunity to explore the intersection of causality and reinforcement learning. Jun 16, 2023 · We demonstrate how to utilize causal diagrams, which may contain bidirectional arcs, to design model-free deep Q-learning-based agents by decomposing the value function per causal variable. Dec 31, 2022 · 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. Bareinboim and S. research pro-posal, we present a way to combine both areas to improve their respective learning processes, espe-cially in the context of our application area (service robotics). May 13, 2024 · Inspired by recent, successful applications of reinforcement learning to knowledge graph tasks, such as link prediction and fact-checking, we explore the application of reinforcement learning on a causality graph for causal question answering. May 4, 2023 · Generating explanations for reinforcement learning (RL) is challenging as actions may produce long-term effects on the future. In recent years, there has been a growing interest in applying reinforcement learning (RL) techniques to order execution owing to RL’s strong sequential decision-making ability. The latter increases the task Aug 1, 2021 · The relation between Reinforcement learning (RL) and Causal Modeling (CM) is an underexplored area with untapped potential for any learning task. Finally, in the acting stage, Causal-aware LLMs exploit struc-tured causal knowledge for more efficient policy-making through the reinforcement learning agent. This work presents an approach for generating policy-level contrastive explanations for MARL to answer a temporal user query, which specifies a sequence of tasks completed by . 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. [doi] Authors BibTeX References Bibliographies Reviews Related Aug 3, 2024 · Data-driven offline reinforcement learning and imitation learning approaches have been gaining popularity in addressing sequential decision-making problems. The tutorial is divided into two parts, crafted to provide participants with a comprehensive understanding of causal reinforcement learning. Subsequently, in the adapting stage, we update the structured causal model through external feedback about the environment, via an idea of causal intervention. Bibliographic details on Ordering-Based Causal Discovery with Reinforcement Learning. In this paper, we propose a dependent multi-task learning framework with the causal intervention (DMTCI). However, searching the space of directed graphs and enforcing acyclicity by implicit May 3, 2025 · Causal Fairness Analysis, ACM Conference on Fairness, Accountability, and Transparency (FaccT), Virtual, with E. D. talk | August 6th at 11:30 | Session: DM: Mining graphs (1/3) [+] More Nevertheless, existing studies typically ignore the confounding effects of geospatial context, leading to the acquisition of spurious correlations and limited generalization capabilities. 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. Finally, in the acting stage, Causal-aware LLMs exploit structured causal knowledge for more efficient policy-making through the reinforcement learning agent. Abstract The relation between Reinforcement learning (RL) and Causal Modeling(CM) is an underexplored area with untapped potential for any learning task. Model-free Causal Reinforcement Learning with Causal Diagrams Junkyu Lee, Tian Gao, Elliot Nelson, Miao Liu, and Debarun Bhattacharjya Oral Abstract Inferring causal protein signaling networks from human immune system cellular data is an impor-tant approach to reveal underlying tissue signaling biology and dysfunction in diseased cells. 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 Abstract It is a long-standing question to discover causal rela-tions among a set of variables in many empirical sci-ences. In contrast, CTRS leverages path discovery and counterfactual inference for superior interpretability. [Gelada and Bellemare, 2019] Carles Gelada and Marc G Bellemare. research Abstract Causal discovery on event sequences holds a piv-otal significance across domains such as health-care, finance, and industrial systems. Despite their intertwined nature, the interaction between these two disciplines has been limited, leaving IJCAI Causal Reinforcement Learning Tutorial. Lee @inproceedings{ijcai2023p505, title = {Explainable Reinforcement Learning via a Causal World Model}, author = {Yu, Zhongwei and Ruan, Jingqing and Xing, Dengpeng}, booktitle = {Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, {IJCAI-23}}, Jul 4, 2023 · Reinforcement learning is an essential paradigm for solving sequential decision problems under uncertainty. However, realistic order execution tasks usually involve a large fine-grained action space and a long trading duration. Whether you are an experienced reinforcement learning researcher, a causal research enthusiast, or a machine learning practitioner eager to expand your toolkit, join us in exploring the exciting world of causal reinforcement learning and advancing this emerging field together. Plecko Causal Reinforcement Learning, International Joint Conference on Artificial Intelligence (IJCAI), Macau China, with E. For more information, check “Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approach”, NeurIPS 2023 P129 Learning curves on a suite of MuJoCo benchmark tasks. org, 2021. In Z. We propose ICM-VAE, a framework for learning causally disentangled A venue for knowledge-based compositional generalization in IJCAI 2023. In this paper, we address this gap in the literature by investigating the Subsequently, in the adapting stage, we update the structured causal model through external feedback about the environ-ment, via an idea of causal intervention. Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence Jeju, Korea 3-9 August 2024 Aug 21, 2023 · The tutorial will categorize and systematically review existing causal reinforcement learning approaches based on their target problems and methodologies. One of the main obstacles is that reinforcement learning agents lack a fundamental understanding of the world and must therefore learn from scratch (2021) Wang et al. Abstract In multi-agent reinforcement learning (MARL), the ε-greedy method plays an important role in balancing exploration and exploitation during the decision-making process in value-based algo-rithms. Finally, in the acting stage, Causal-Aware LLMs exploit structured causal knowledge for more efficient policy-making through the reinforcement learning agent. Research Interests Causal Inference: Theory and Applications. Bareinboim and D. talk | August 7th at 15:00 | Session: NLP: Natural Language Processing (2/3) [+] More 223 Graph Contrastive Learning with Reinforcement Augmentation Ziyang Liu, Chaokun Wang, Cheng Wu 6 min. An Interpretable Deep Reinforcement Learning Approach to Autonomous Driving Zhihao Song, Yunpeng Jiang, Jianyi Zhang, Paul Weng, Dong Li, Wulong Liu, Jianye Hao Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems, trains an agent to achieve different goals under particular scenarios. In this work, to explore how biases are formed, we revisit LLMs’ text generation from a causal perspective. For the corresponding material, check out the links: The Causal Reinforcement Learning Workshop Website Abstract: Reinforcement Learning (RL) and Causal Inference have developed as largely independent fields, yet they share a fundamental connection: both aim to model and reason about how actions influence outcomes in uncertain environments. The crux of this endeavor lies in unraveling causal struc-tures among event types, typically portrayed as di-rected acyclic graphs (DAGs). Previous model-based offline RL methods employ a straightforward prediction method that maps the states and actions directly to the next-step states. Recently, Reinforcement Learning (RL) has achieved promising results in causal Abstract Generating explanations for reinforcement learning (RL) is challenging as actions may produce long-term effects on the future. Existing methods for learning this graph primarily focus on agent-pair relations, neglecting higher-order relationships. This work presents an approach for generating policy-level contrastive explanations for MARL to answer a temporal user query, which specifies a sequence of tasks completed by Aug 3, 2024 · To tackle this challenge, we introduce causal exploration in this paper, a strategy that leverages the underlying causal knowledge for both data collection and model training. g. , visual features of "long hair" and "woman"). Why the Agent Made that Decision: Contrastive Explanation Learning for Reinforcement Learning Rui Zuo, Simon Khan, Zifan Wang, Garrett Ethan Katz, Qinru Qiu (PDF | Details) Computer Vision Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation Niaz Ahmad, Jawad Khan, Kang G. Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation Jun 28, 2021 · Learning efficiently a causal model of the environment is a key challenge of model-based RL agents operating in POMDPs. However, observational data may mislead the The relation between Reinforcement learning (RL) and Causal Modeling (CM) is an underexplored area with untapped potential for any learning task. e. It is a long-standing question to discover causal rela- tions among a set of variables in many empirical sci- ences. pages 3566-3573, ijcai. Artificial Intelligence, Machine Learning, Statistics. As we will see, there are many ways that causal inference can be integrated across many Abstract Over recent decades, sequential decision-making tasks are mostly tackled with expert systems and reinforcement learning. The preliminary results obtained so Abstract Generating explanations for reinforcement learning (RL) is challenging as actions may produce long-term effects on the future. Cognitive Science, Philosophy of Science. May 18, 2021 · In this paper, we propose a dependent multi-task learning framework with the causal intervention (DMTCI). To leverage more samples, we consider incorporating data from all individuals as population data. #tutorial Towards a new wave of reinforcement learning at #T28: Towards Causal Reinforcement Learning: Empowering Agents with Causality by Zhihong Deng, Jing… Nov 28, 2022 · 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. In this section, we aim to explore and explain the inequality in reinforcement learning through a causal lens. Abstract Communication can impressively improve co-operation in multi-agent reinforcement learning (MARL), especially for partially-observed tasks. Learning and Inference for Structured Prediction: A Unifying Perspective: Jana Doppa, Aryan Deshwal, Dan Roth Leveraging Human Guidance for Deep Reinforcement Learning Tasks: Ruohan Zhang, Faraz Torabi, Lin Guan, Dana H. However, searching the space of directed graphs and enforcing acyclicity by implicit penalties tend to be inefcient and restrict the exist- ing RL-based method to Dec 14, 2024 · Model-based methods have recently been shown promising for offline reinforcement learning (RL), which aims at learning good policies from historical data without interacting with the environment. We propose a novel approach for generating Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence Melbourne, Australia 19-25 August 2017 On the other hand, reinforcement learning excels at learning end-to-end policies directly from sensory inputs in unstructured environments but struggles with com-positional generalization in complex tasks with de-layed rewards. Achieving fairness in learning models is currently an imperative task in machine learning. Offline reinforcement learning promises to alleviate this issue by exploiting the vast amount of observational data available in the real world. In Proceedings of the 32nd International Joint Conference on Artificial Intelligence, pages 5702-5710, 2023. Counterfactual explanations, which address “what if” scenarios, provide a promising avenue for understanding RL decisions but remain underexplored for continuous action spaces. arXiv preprint arXiv:2106. Abstract: It is a long-standing question to discover causal relations among a set of variables in many empirical sciences. Deep reinforcement learning (DRL) requires the collection of interventional data, which is some-times expensive and even unethical in the real world, such as in the autonomous driving and the medical field. In sequential decision-making problems involving sensitive attributes like race and gender, reinforcement learning (RL) agents must carefully consider long-term fairness while maximizing returns. The preliminary results Jun 21, 2025 · Published: 21 Jun 2025, Last Modified: 18 Aug 2025 IJCAI2025 workshop Causal Learning for Recommendation Systems Readers: Everyone 0 Replies Aug 19, 2023 · Unlike the majority of approaches in causal reinforcement learning that focus on model-based approaches and off-policy evaluations, we explore another direction: online model-free methods. Compared to the standard RL solutions that learn a policy solely depending on the states or observations, GCRL additionally requires the agent to make decisions according to different goals. However, existing works either broadcast the mes-sages leading to information redundancy, or learn targeted communication by modeling all the other agents as targets, which is not scalable when the number of agents varies. It is a long-standing question to discover causal relations among a set of variables in many empirical sciences. ), Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021 (pp. Lee, J. 3566-3573). In this work, we define a new notion of causal disentanglement from the perspective of indepen-dent causal mechanisms. Aug 19, 2023 · Abstract Generating explanations for reinforcement learning (RL) is challenging as actions may produce long-term effects on the future. Firstly, we involve an intermediate task, bag-of-categories generation, before the final task, image captioning. Aug 19, 2023 · Generating explanations for reinforcement learning (RL) is challenging as actions may produce long-term effects on the future. In Zhi-Hua Zhou, editor, Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event / Montreal, Canada, 19-27 August 2021. The model captures the influence of actions, allowing us to interpret the long-term effects of actions In recent years, there has been a growing interest in applying reinforcement learning (RL) techniques to order execution owing to RL’s strong sequential decision-making ability. Nonetheless, pre-vailing methodologies often grapple with unten-able assumptions and intricate optimization hur A Causality Reward Redistribution Methods for MARL. Plus, our tutorial on Causal RL is also accepted! See you in Jeju, Korea! I’m grateful to receive the Student Best Paper Award from the Australian Artificial Intelligence Institute. In this work, to tackle We use Multi-Agent Deep Q Learning with double Q learn-ing and dueling network as the basic reinforcement learning structure. Ballard, Peter Stone Recent Advances in Imitation Learning from Observation: Faraz Torabi, Garrett Warnell, Peter Stone Abstract Reinforcement Learning (RL) has shown great promise in domains like healthcare and robotics but often struggles with adoption due to its lack of in-terpretability. Recently, Reinforcement Learning (RL) has achieved promising results in causal discovery from observ I am also thrilled to present a tutorial, "Causal Reinforcement Learning: Empowering Agents with Causality," at the same conference. The latter increases the task Aug 19, 2023 · 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. In recent years, reinforcement learning (RL) methods have shown excellent performance in the field of causal protein signaling network inference. 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 Goals of This Tutorial Introduce basic concepts of causality and reinforcement learning. Zhang), International Joint Conference on Artificial Intelligence (IJCAI), Macau, China, Aug/2019. May 1, 2024 · #IJCAItutorial T28: Towards Causal Reinforcement Learning: Empowering Agents with Causality #IJCAI2024 🗣️Zhihong Deng, Jing Jiang, Chengqi Zhang ️ May 13, 2024 · Inspired by recent, successful applications of reinforcement learning to knowledge graph tasks, such as link prediction and fact-checking, we explore the application of reinforcement learning on a causality graph for causal question answering. net Abstract Generating explanations for reinforcement learning (RL) is challenging as actions may produce long-term effects on the future. 2025/4/24, one paper 'Learning by doing: an online causal reinforcement learning framework with causal-aware policy' has been accepted by SCIENCE CHINA Information Sciences! Subsequently, we propose Continual Multi-Objective Reinforcement Learning via Reward Model Rehearsal (CORe3), incorporating a dynamic agent network for rapid adaptation to new objectives. "Introduction to Causal Inference", Machine Learning Research School (MLRS), Bangkok, Thailand, Aug/2019. We propose a novel approach for generating 😉 🎓 Google Scholar 📘 Zhihu 🌵 Github 👨‍🎓 CV 🤝 Linkedin 🔥 News Our paper on fairness in reinforcement learning is accepted by IJCAI 2024. We, in particular, focus on enhancing the sample efficiency and reliability of the world model learning within the domain of task-agnostic reinforcement learning. In this article, we dis-cuss a novel paradigm that leverages Transformer-based sequence models to tackle decision-making tasks, named large decision models In sequential decision-making problems involving sensitive attributes like race and gender, reinforcement learning (RL) agents must carefully consider long-term fairness while maximizing returns. Ordering-Based Causal Discovery with Reinforcement Learning. Aug 19, 2023 · As multi-agent reinforcement learning (MARL) systems are increasingly deployed throughout society, it is imperative yet challenging for users to understand the emergent behaviors of MARL agents in complex environments. However, searching the space of directed graphs and enforcing acyclicity by implicit penalties tend to be inefficient and restrict the exist-ing RL-based Aug 19, 2023 · 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. We ob-serve that the variables of all Abstract Learning disentangled causal representations is a challenging problem that has gained significant at-tention recently due to its implications for extract-ing meaningful information for downstream tasks. However, observational data may mislead the Generating explanations for reinforcement learning (RL) is challenging as actions may produce long-term effects on the future. Bibliographic details on Combining Reinforcement Learning and Causal Models for Robotics Applications. How causality information inspires current RL algorithms is illustrated in the below CRL framework, CRL Dependent Multi-Task Learning with Causal Intervention for Image Captioning Wenqing Chen1;2 , Jidong Tian1;2 , Caoyun Fan1;2 , Hao He1;2 and Yaohui Jin1;2 1MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University 2State Key Lab of Advanced Optical Communication System and Network, Shanghai Jiao Tong University {wenqingchen, frank92, fcy3649, hehao, jinyh}@sjtu. Generating explanations for reinforcement learning (RL) is challenging as actions may produce long-term effects on the future. Discuss what causal reinforcement learning is and how it is different from traditional reinforcement learning. We propose a multi-agent reinforcement learning ap-proach to address the CSC problem, and subsequently introduce a causal-perspective reward redistribution scheme that leverages Bayesian surprise to quantify in-dividual agent contributions. Inferring causal protein signaling networks from human immune system cellular data is an important approach to reveal underlying tissue signaling biology and dysfunction in diseased cells. In this survey, we provide a comprehensive Ordering-Based Causal Discovery with Reinforcement Learning. Keywords: Causal reinforcement learning, High-level action model, action space generalization, Value decomposition network, Markov Decision Process with Unobserved Confounders TL;DR: We demonstrate how to utilize causal diagrams, which may contain bidirectional arcs, to design model-free deep Q-learning-based agents by decomposing the value function per causal variable. International Joint Conferences on Artificial Intelligence. This becomes particularly marked due to practical limitations in obtaining comprehensive datasets for all preferences, where Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning Guojia Wan1 , Shirui Pan2 , Chen Gong3;4 , Chuan Zhou5 , Gholamreza Haffari2 1School of Computer Science, Institute of Artificial Intelligence, and National Engineering Research Center for Multimedia Software, Wuhan University, China 2Faculty of Information Technology, Monash University, Australia 3School Aug 3, 2024 · Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information for downstream tasks. May 14, 2021 · This paper presents CORE, a deep reinforcement learning-based approach for causal discovery and intervention planning that learns to sequentially reconstruct causal graphs from data while learning to perform informative interventions. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence Macao, SAR 19-25 August 2023 Our tutorial delves into this fascinating research area, offering participants a unique opportunity to explore the intersection of causality and reinforcement learning. The relation between Reinforcement learning (RL) and Causal Modeling (CM) is an underexplored area with untapped potential for any learning task. We consider here a scenario where the learning agent has the ability to collect online experiences through direct interactions with the environment (interventional data), but has also access to a large collection of offline experiences, obtained by observing another agent Graph contrastive learning (GCL), designing contrastive objectives to learn embeddings from augmented graphs, has become a prevailing method for extracting embeddings from graphs in an unsupervised manner. 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. However, the ε-greedy exploration process will introduce conservativeness when calculating the expected state value when the agents are more in need of exploitation during the approximate Aug 1, 2023 · Distilling Deep Reinforcement Learning Policies in Soft Decision Trees. Yet, these approaches rarely consider learning Pareto-optimal policies from a limited pool of expert datasets. Despite many remarkable achievements in recent decades, applying reinforcement learning methods in the real world remains challenging. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence Macao, 10-16 August 2019 To tackle this challenge, we introduce causal exploration in this paper, a strategy that leverages the underlying causal knowledge for both data collection and model training. Ofline reinforcement learning promises to alleviate this issue by exploiting the vast amount of observational data available in the real world. (IJCAI International Joint Conference on Artificial Intelligence). However, observational data may mislead the Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Vienna 23-29 July 2022 Abstract Learning the causal structure of each individual plays a crucial role in neuroscience, biology, and so on. To bridge this gap, we initially formulate a Structural Causal Model (SCM) to decipher the trajectory representation learning process from a causal perspective. The former hinders the RL agents from efficient exploration. (2023) Yu et al. We first present the problem formulation, employing causal modeling to anal-yse environmental dynamics in sequential decision-making scenarios. In this paper, we address this gap in the literature by investigating the Structural hawkes processes for learning causal structure from discrete-time event sequences. In this work, we define a new notion of causal disentanglement from the perspective of independent causal mechanisms. , confounders). However, such a prediction method tends to capture To tackle this challenge, we introduce causal exploration in this paper, a strategy that leverages the underlying causal knowledge for both data collection and model training. Meanwhile, recent research showed that fairness should be studied from the causal perspective, and proposed a number of fairness criteria based on Pearl's causal modeling framework. However, observational data may mislead the Ordering-Based Causal Discovery with Reinforcement Learning: Paper and Code. Recently, Reinforcement Learning (RL) has achieved promising results in causal discovery from observational data. Then, we propose a general yet simple methodology for leveraging offline data during learning. In this paper, we investigate the problem of building causal fairness-aware generative adversarial networks (CFGAN), which 6 min. Shin, Youngmoon Lee, Guanghui Wang (PDF | Details) This repository lists papers on causal for machine learning application. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence Montreal, 19-27 August 2021 Whether you are an experienced reinforcement learning researcher, a causal research enthusiast, or a machine learning practitioner eager to expand your toolkit, join us in exploring the exciting world of causal reinforcement learning and advancing this emerging field together. The model captures the influence of actions, allowing us to interpret the long-term effects of actions Deep reinforcement learning (DRL) requires the collection of interventional data, which is some-times expensive and even unethical in the real world, such as in the autonomous driving and the medical field. In this paper, we propose two deconfounding methods in DRL to address this problem. Aug 1, 2021 · Request PDF | Ordering-Based Causal Discovery with Reinforcement Learning | It is a long-standing question to discover causal relations among a set of variables in many empirical sciences Feb 6, 2025 · Causal Reinforcement Learning This page provides information and materials about " Causal Reinforcement Learning " (CRL), following the tutorial presented at ICML 2020. We propose ICM-VAE, a framework for learning causally Nov 1, 2023 · A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges - Plankson/awesome-explainable-reinforcement-learning In sequential decision-making problems involving sensitive attributes like race and gender, reinforcement learning (RL) agents must carefully consider long-term fairness while maximizing returns. In this paper, we develop novel framework for explainable RL by learning causal world model without prior knowledge of the causal structure of the environment. However, searching the space of directed graphs and enforcing acyclicity by implicit penalties tend to be inefcient and restrict the exist- ing RL-based method to The application of causal methods within reinforcement learning is an area of active research and early adoption. The preliminary results obtained so 257: Causal Learning Meet Covariates: Empowering Lightweight and Effective Nationwide Air Quality Forecasting Preprint Authors: Jiaming Ma, Zhiqing Cui, Binwu Wang, Pengkun Wang, Zhengyang Zhou, Zhe Zhao, Yang Wang Location: Guangzhou | Day: August 31st | Time: 14:45 | Session: DM: Mining spatial and/or temporal data May 14, 2021 · It is a long-standing question to discover causal relations among a set of variables in many empirical sciences. Carlos Martin, Tuomas Sandholm [+] More 339 SeRO: Self-Supervised Reinforcement Learning for Recovery from Out-of-Distribution Situations Chan Kim, Jaekyung Cho, Christophe Bobda, Seung-Woo Seo, Seong-Woo Kim [+] More 345 "Causal Reinforcement Learning" (with S. #IJCAItutorial T28: Towards Causal Reinforcement Learning: Empowering Agents with Causality #IJCAI2024 🗣️Zhihong Deng, Jing Jiang, Chengqi Zhang This paper introduces the Temporal-Logic-based Causal Diagram (TL-CD) in reinforcement learning (RL) to address the limitations of traditional RL algo-rithms that use finite state machines to represent temporally extended goals. In Proceedings of the IJCAI 2019 Workshop on Explainable Artificial Intelligence, pages 1-6, 2019. May 30, 2025 · Subsequently,in the adapting stage, we update the structured causal model through external feedback about the environment, via an idea of causal intervention. RS models, especially deep learning-based ones, often lack interpretability, and thus can hardly offer clues about the reason for recommendations. Figure 2 illustrates the newly proposed model, and the pseudo codes of UniComm and UniLight are presented in our technique report [Jiang et al. Zhou (Ed. Contributor: Anpeng Wu. 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). 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. From a causal perspective, the reason is that models have captured spurious statistical correlations between visual features and certain expressions (e. Existing methods consider data from each individual separately, which may yield inaccurate causal structure estimations in limited samples. 14421, 2021. We will also outline open issues and future research directions to foster the continuous development and application of causal reinforcement learning in real-world scenarios. dwjlq eihqbu icuoctc kyawm abxse mktgv drgw dlhydsp jvz piki