Auswahl der wissenschaftlichen Literatur zum Thema „Goal-conditioned reinforcement learning“
Geben Sie eine Quelle nach APA, MLA, Chicago, Harvard und anderen Zitierweisen an
Inhaltsverzeichnis
Machen Sie sich mit den Listen der aktuellen Artikel, Bücher, Dissertationen, Berichten und anderer wissenschaftlichen Quellen zum Thema "Goal-conditioned reinforcement learning" bekannt.
Neben jedem Werk im Literaturverzeichnis ist die Option "Zur Bibliographie hinzufügen" verfügbar. Nutzen Sie sie, wird Ihre bibliographische Angabe des gewählten Werkes nach der nötigen Zitierweise (APA, MLA, Harvard, Chicago, Vancouver usw.) automatisch gestaltet.
Sie können auch den vollen Text der wissenschaftlichen Publikation im PDF-Format herunterladen und eine Online-Annotation der Arbeit lesen, wenn die relevanten Parameter in den Metadaten verfügbar sind.
Zeitschriftenartikel zum Thema "Goal-conditioned reinforcement learning"
Yin, Xiangyu, Sihao Wu, Jiaxu Liu, Meng Fang, Xingyu Zhao, Xiaowei Huang und Wenjie Ruan. „Representation-Based Robustness in Goal-Conditioned Reinforcement Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 38, Nr. 19 (24.03.2024): 21761–69. http://dx.doi.org/10.1609/aaai.v38i19.30176.
Der volle Inhalt der QuelleLevine, Alexander, und Soheil Feizi. „Goal-Conditioned Q-learning as Knowledge Distillation“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 7 (26.06.2023): 8500–8509. http://dx.doi.org/10.1609/aaai.v37i7.26024.
Der volle Inhalt der QuelleYAMADA, Takaya, und Koich OGAWARA. „Goal-Conditioned Reinforcement Learning with Latent Representations using Contrastive Learning“. Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2021 (2021): 1P1—I15. http://dx.doi.org/10.1299/jsmermd.2021.1p1-i15.
Der volle Inhalt der QuelleQian, Zhifeng, Mingyu You, Hongjun Zhou und Bin He. „Weakly Supervised Disentangled Representation for Goal-Conditioned Reinforcement Learning“. IEEE Robotics and Automation Letters 7, Nr. 2 (April 2022): 2202–9. http://dx.doi.org/10.1109/lra.2022.3141148.
Der volle Inhalt der QuelleTANIGUCHI, Asuto, Fumihiro SASAKI und Ryota YAMASHINA. „Goal-Conditioned Reinforcement Learning with Extended Floyd-Warshall method“. Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2020 (2020): 2A1—L01. http://dx.doi.org/10.1299/jsmermd.2020.2a1-l01.
Der volle Inhalt der QuelleElguea-Aguinaco, Íñigo, Antonio Serrano-Muñoz, Dimitrios Chrysostomou, Ibai Inziarte-Hidalgo, Simon Bøgh und Nestor Arana-Arexolaleiba. „Goal-Conditioned Reinforcement Learning within a Human-Robot Disassembly Environment“. Applied Sciences 12, Nr. 22 (15.11.2022): 11610. http://dx.doi.org/10.3390/app122211610.
Der volle Inhalt der QuelleLiu, Bo, Yihao Feng, Qiang Liu und Peter Stone. „Metric Residual Network for Sample Efficient Goal-Conditioned Reinforcement Learning“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 7 (26.06.2023): 8799–806. http://dx.doi.org/10.1609/aaai.v37i7.26058.
Der volle Inhalt der QuelleDing, Hongyu, Yuanze Tang, Qing Wu, Bo Wang, Chunlin Chen und Zhi Wang. „Magnetic Field-Based Reward Shaping for Goal-Conditioned Reinforcement Learning“. IEEE/CAA Journal of Automatica Sinica 10, Nr. 12 (Dezember 2023): 2233–47. http://dx.doi.org/10.1109/jas.2023.123477.
Der volle Inhalt der QuelleXu, Jiawei, Shuxing Li, Rui Yang, Chun Yuan und Lei Han. „Efficient Multi-Goal Reinforcement Learning via Value Consistency Prioritization“. Journal of Artificial Intelligence Research 77 (05.06.2023): 355–76. http://dx.doi.org/10.1613/jair.1.14398.
Der volle Inhalt der QuelleFaccio, Francesco, Vincent Herrmann, Aditya Ramesh, Louis Kirsch und Jürgen Schmidhuber. „Goal-Conditioned Generators of Deep Policies“. Proceedings of the AAAI Conference on Artificial Intelligence 37, Nr. 6 (26.06.2023): 7503–11. http://dx.doi.org/10.1609/aaai.v37i6.25912.
Der volle Inhalt der QuelleDissertationen zum Thema "Goal-conditioned reinforcement learning"
Chenu, Alexandre. „Leveraging sequentiality in Robot Learning : Application of the Divide & Conquer paradigm to Neuro-Evolution and Deep Reinforcement Learning“. Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS342.
Der volle Inhalt der Quelle“To succeed, planning alone is insufficient. One must improvise as well.” This quote from Isaac Asimov, founding father of robotics and author of the Three Laws of Robotics, emphasizes the importance of being able to adapt and think on one’s feet to achieve success. Although robots can nowadays resolve highly complex tasks, they still need to gain those crucial adaptability skills to be deployed on a larger scale. Robot Learning uses learning algorithms to tackle this lack of adaptability and to enable robots to solve complex tasks autonomously. Two types of learning algorithms are particularly suitable for robots to learn controllers autonomously: Deep Reinforcement Learning and Neuro-Evolution. However, both classes of algorithms often cannot solve Hard Exploration Problems, that is problems with a long horizon and a sparse reward signal, unless they are guided in their learning process. One can consider different approaches to tackle those problems. An option is to search for a diversity of behaviors rather than a specific one. The idea is that among this diversity, some behaviors will be able to solve the task. We call these algorithms Diversity Search algorithms. A second option consists in guiding the learning process using demonstrations provided by an expert. This is called Learning from Demonstration. However, searching for diverse behaviors or learning from demonstration can be inefficient in some contexts. Indeed, finding diverse behaviors can be tedious if the environment is complex. On the other hand, learning from demonstration can be very difficult if only one demonstration is available. This thesis attempts to improve the effectiveness of Diversity Search and Learning from Demonstration when applied to Hard Exploration Problems. To do so, we assume that complex robotics behaviors can be decomposed into reaching simpler sub-goals. Based on this sequential bias, we try to improve the sample efficiency of Diversity Search and Learning from Demonstration algorithms by adopting Divide & Conquer strategies, which are well-known for their efficiency when the problem is composable. Throughout the thesis, we propose two main strategies. First, after identifying some limitations of Diversity Search algorithms based on Neuro-Evolution, we propose Novelty Search Skill Chaining. This algorithm combines Diversity Search with Skill- Chaining to efficiently navigate maze environments that are difficult to explore for state-of-the-art Diversity Search. In a second set of contributions, we propose the Divide & Conquer Imitation Learning algorithms. The key intuition behind those methods is to decompose the complex task of learning from a single demonstration into several simpler goal-reaching sub-tasks. DCIL-II, the most advanced variant, can learn walking behaviors for under-actuated humanoid robots with unprecedented efficiency. Beyond underlining the effectiveness of the Divide & Conquer paradigm in Robot Learning, this work also highlights the difficulties that can arise when composing behaviors, even in elementary environments. One will inevitably have to address these difficulties before applying these algorithms directly to real robots. It may be necessary for the success of the next generations of robots, as outlined by Asimov
Buchteile zum Thema "Goal-conditioned reinforcement learning"
Steccanella, Lorenzo, und Anders Jonsson. „State Representation Learning for Goal-Conditioned Reinforcement Learning“. In Machine Learning and Knowledge Discovery in Databases, 84–99. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26412-2_6.
Der volle Inhalt der QuelleZou, Qiming, und Einoshin Suzuki. „Contrastive Goal Grouping for Policy Generalization in Goal-Conditioned Reinforcement Learning“. In Neural Information Processing, 240–53. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92185-9_20.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Goal-conditioned reinforcement learning"
Liu, Minghuan, Menghui Zhu und Weinan Zhang. „Goal-Conditioned Reinforcement Learning: Problems and Solutions“. In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/770.
Der volle Inhalt der QuelleBortkiewicz, Michał, Jakub Łyskawa, Paweł Wawrzyński, Mateusz Ostaszewski, Artur Grudkowski, Bartłomiej Sobieski und Tomasz Trzciński. „Subgoal Reachability in Goal Conditioned Hierarchical Reinforcement Learning“. In 16th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012326200003636.
Der volle Inhalt der QuelleYu, Zhe, Kailai Sun, Chenghao Li, Dianyu Zhong, Yiqin Yang und Qianchuan Zhao. „A Goal-Conditioned Reinforcement Learning Algorithm with Environment Modeling“. In 2023 42nd Chinese Control Conference (CCC). IEEE, 2023. http://dx.doi.org/10.23919/ccc58697.2023.10240963.
Der volle Inhalt der QuelleZou, Qiming, und Einoshin Suzuki. „Sample-Efficient Goal-Conditioned Reinforcement Learning via Predictive Information Bottleneck for Goal Representation Learning“. In 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023. http://dx.doi.org/10.1109/icra48891.2023.10161213.
Der volle Inhalt der QuelleDeng, Yuhong, Chongkun Xia, Xueqian Wang und Lipeng Chen. „Deep Reinforcement Learning Based on Local GNN for Goal-Conditioned Deformable Object Rearranging“. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. http://dx.doi.org/10.1109/iros47612.2022.9981669.
Der volle Inhalt der QuelleBagaria, Akhil, und Tom Schaul. „Scaling Goal-based Exploration via Pruning Proto-goals“. In 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.2023/384.
Der volle Inhalt der QuelleSimmons-Edler, Riley, Ben Eisner, Daniel Yang, Anthony Bisulco, Eric Mitchell, Sebastian Seung und Daniel Lee. „Reward Prediction Error as an Exploration Objective in Deep RL“. In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/390.
Der volle Inhalt der Quelle