Littérature scientifique sur le sujet « Sparsely rewarded environments »
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Articles de revues sur le sujet "Sparsely rewarded environments"
Dubey, Rachit, Thomas L. Griffiths et Peter Dayan. « The pursuit of happiness : A reinforcement learning perspective on habituation and comparisons ». PLOS Computational Biology 18, no 8 (4 août 2022) : e1010316. http://dx.doi.org/10.1371/journal.pcbi.1010316.
Texte intégralShi, Xiaoping, Shiqi Zou, Shenmin Song et Rui Guo. « A multi-objective sparse evolutionary framework for large-scale weapon target assignment based on a reward strategy ». Journal of Intelligent & ; Fuzzy Systems 40, no 5 (22 avril 2021) : 10043–61. http://dx.doi.org/10.3233/jifs-202679.
Texte intégralSakamoto, Yuma, et Kentarou Kurashige. « Self-Generating Evaluations for Robot’s Autonomy Based on Sensor Input ». Machines 11, no 9 (6 septembre 2023) : 892. http://dx.doi.org/10.3390/machines11090892.
Texte intégralParisi, Simone, Davide Tateo, Maximilian Hensel, Carlo D’Eramo, Jan Peters et Joni Pajarinen. « Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning ». Algorithms 15, no 3 (28 février 2022) : 81. http://dx.doi.org/10.3390/a15030081.
Texte intégralMguni, David, Taher Jafferjee, Jianhong Wang, Nicolas Perez-Nieves, Wenbin Song, Feifei Tong, Matthew Taylor et al. « Learning to Shape Rewards Using a Game of Two Partners ». Proceedings of the AAAI Conference on Artificial Intelligence 37, no 10 (26 juin 2023) : 11604–12. http://dx.doi.org/10.1609/aaai.v37i10.26371.
Texte intégralForbes, Grant C., et David L. Roberts. « Potential-Based Reward Shaping for Intrinsic Motivation (Student Abstract) ». Proceedings of the AAAI Conference on Artificial Intelligence 38, no 21 (24 mars 2024) : 23488–89. http://dx.doi.org/10.1609/aaai.v38i21.30441.
Texte intégralXu, Pei, Junge Zhang, Qiyue Yin, Chao Yu, Yaodong Yang et Kaiqi Huang. « Subspace-Aware Exploration for Sparse-Reward Multi-Agent Tasks ». Proceedings of the AAAI Conference on Artificial Intelligence 37, no 10 (26 juin 2023) : 11717–25. http://dx.doi.org/10.1609/aaai.v37i10.26384.
Texte intégralKubovčík, Martin, Iveta Dirgová Luptáková et Jiří Pospíchal. « Signal Novelty Detection as an Intrinsic Reward for Robotics ». Sensors 23, no 8 (14 avril 2023) : 3985. http://dx.doi.org/10.3390/s23083985.
Texte intégralCatacora Ocana, Jim Martin, Roberto Capobianco et Daniele Nardi. « An Overview of Environmental Features that Impact Deep Reinforcement Learning in Sparse-Reward Domains ». Journal of Artificial Intelligence Research 76 (26 avril 2023) : 1181–218. http://dx.doi.org/10.1613/jair.1.14390.
Texte intégralZhou, Xiao, Song Zhou, Xingang Mou et Yi He. « Multirobot Collaborative Pursuit Target Robot by Improved MADDPG ». Computational Intelligence and Neuroscience 2022 (25 février 2022) : 1–10. http://dx.doi.org/10.1155/2022/4757394.
Texte intégralThèses sur le sujet "Sparsely rewarded environments"
Gallouedec, Quentin. « Toward the generalization of reinforcement learning ». Electronic Thesis or Diss., Ecully, Ecole centrale de Lyon, 2024. http://www.theses.fr/2024ECDL0013.
Texte intégralConventional Reinforcement Learning (RL) involves training a unimodal agent on a single, well-defined task, guided by a gradient-optimized reward signal. This framework does not allow us to envisage a learning agent adapted to real-world problems involving diverse modality streams, multiple tasks, often poorly defined, sometimes not defined at all. Hence, we advocate for transitioning towards a more general framework, aiming to create RL algorithms that more inherently versatile.To advance in this direction, we identify two primary areas of focus. The first aspect involves improving exploration, enabling the agent to learn from the environment with reduced dependence on the reward signal. We present Latent Go-Explore (LGE), an extension of the Go-Explore algorithm. While Go-Explore achieved impressive results, it was constrained by domain-specific knowledge. LGE overcomes these limitations, offering wider applicability within a general framework. In various tested environments, LGE consistently outperforms the baselines, showcasing its enhanced effectiveness and versatility. The second focus is to design a general-purpose agent that can operate in a variety of environments, thus involving a multimodal structure and even transcending the conventional sequential framework of RL. We introduce Jack of All Trades (JAT), a multimodal Transformer-based architecture uniquely tailored to sequential decision tasks. Using a single set of weights, JAT demonstrates robustness and versatility, competing its unique baseline on several RL benchmarks and even showing promising performance on vision and textual tasks. We believe that these two contributions are a valuable step towards a more general approach to RL. In addition, we present other methodological and technical advances that are closely related to our core research question. The first is the introduction of a set of sparsely rewarded simulated robotic environments designed to provide the community with the necessary tools for learning under conditions of low supervision. Notably, three years after its introduction, this contribution has been widely adopted by the community and continues to receive active maintenance and support. On the other hand, we present Open RL Benchmark, our pioneering initiative to provide a comprehensive and fully tracked set of RL experiments, going beyond typical data to include all algorithm-specific and system metrics. This benchmark aims to improve research efficiency by providing out-of-the-box RL data and facilitating accurate reproducibility of experiments. With its community-driven approach, it has quickly become an important resource, documenting over 25,000 runs.These technical and methodological advances, along with the scientific contributions described above, are intended to promote a more general approach to Reinforcement Learning and, we hope, represent a meaningful step toward the eventual development of a more operative RL agent
Hanski, Jari, et Kaan Baris Biçak. « An Evaluation of the Unity Machine Learning Agents Toolkit in Dense and Sparse Reward Video Game Environments ». Thesis, Uppsala universitet, Institutionen för speldesign, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444982.
Texte intégralChapitres de livres sur le sujet "Sparsely rewarded environments"
Hensel, Maximilian. « Exploration Methods in Sparse Reward Environments ». Dans Reinforcement Learning Algorithms : Analysis and Applications, 35–45. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-41188-6_4.
Texte intégralMoy, Glennn, et Slava Shekh. « Evolution Strategies for Sparse Reward Gridworld Environments ». Dans AI 2022 : Advances in Artificial Intelligence, 266–78. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-22695-3_19.
Texte intégralJeewa, Asad, Anban W. Pillay et Edgar Jembere. « Learning to Generalise in Sparse Reward Navigation Environments ». Dans Artificial Intelligence Research, 85–100. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66151-9_6.
Texte intégralChen, Zhongpeng, et Qiang Guan. « Continuous Exploration via Multiple Perspectives in Sparse Reward Environment ». Dans Pattern Recognition and Computer Vision, 57–68. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8435-0_5.
Texte intégralLe, Bang-Giang, Thi-Linh Hoang, Hai-Dang Kieu et Viet-Cuong Ta. « Structural and Compact Latent Representation Learning on Sparse Reward Environments ». Dans Intelligent Information and Database Systems, 40–51. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5837-5_4.
Texte intégralKang, Yongxin, Enmin Zhao, Yifan Zang, Kai Li et Junliang Xing. « Towards a Unified Benchmark for Reinforcement Learning in Sparse Reward Environments ». Dans Communications in Computer and Information Science, 189–201. Singapore : Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1639-9_16.
Texte intégralLiu, Xi, Long Ma, Zhen Chen, Changgang Zheng, Ren Chen, Yong Liao et Shufan Yang. « A Novel State Space Exploration Method for the Sparse-Reward Reinforcement Learning Environment ». Dans Artificial Intelligence XL, 216–21. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47994-6_18.
Texte intégralXie, Zaipeng, Yufeng Zhang, Chentai Qiao et Sitong Shen. « IPERS : Individual Prioritized Experience Replay with Subgoals for Sparse Reward Multi-Agent Reinforcement Learning ». Dans Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230586.
Texte intégralShah, Syed Ihtesham Hussain, Antonio Coronato et Muddasar Naeem. « Inverse Reinforcement Learning Based Approach for Investigating Optimal Dynamic Treatment Regime ». Dans Ambient Intelligence and Smart Environments. IOS Press, 2022. http://dx.doi.org/10.3233/aise220052.
Texte intégralAbate, Alessandro, Yousif Almulla, James Fox, David Hyland et Michael Wooldridge. « Learning Task Automata for Reinforcement Learning Using Hidden Markov Models ». Dans Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230247.
Texte intégralActes de conférences sur le sujet "Sparsely rewarded environments"
Camacho, Alberto, Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Valenzano et Sheila A. McIlraith. « LTL and Beyond : Formal Languages for Reward Function Specification in Reinforcement Learning ». Dans Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California : International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/840.
Texte intégralBougie, Nicolas, et Ryutaro Ichise. « Towards High-Level Intrinsic Exploration in Reinforcement Learning ». Dans 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.2020/733.
Texte intégralWan, Shanchuan, Yujin Tang, Yingtao Tian et Tomoyuki Kaneko. « DEIR : Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards ». 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.2023/477.
Texte intégralNoever, David, et Ryerson Burdick. « Puzzle Solving without Search or Human Knowledge : An Unnatural Language Approach ». Dans 9th International Conference on Artificial Intelligence and Applications (AIAPP 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120902.
Texte intégralChatterjee, Palash, Ashutosh Chapagain, Weizhe Chen et Roni Khardon. « DiSProD : Differentiable Symbolic Propagation of Distributions for Planning ». 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.2023/591.
Texte intégralXu, Pei, Junge Zhang et Kaiqi Huang. « Exploration via Joint Policy Diversity for Sparse-Reward Multi-Agent Tasks ». 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.2023/37.
Texte intégralMemarian, Farzan, Wonjoon Goo, Rudolf Lioutikov, Scott Niekum et Ufuk Topcu. « Self-Supervised Online Reward Shaping in Sparse-Reward Environments ». Dans 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2021. http://dx.doi.org/10.1109/iros51168.2021.9636020.
Texte intégralLin, Xingyu, Pengsheng Guo, Carlos Florensa et David Held. « Adaptive Variance for Changing Sparse-Reward Environments ». Dans 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8793650.
Texte intégralSeurin, Mathieu, Florian Strub, Philippe Preux et Olivier Pietquin. « Don’t Do What Doesn’t Matter : Intrinsic Motivation with Action Usefulness ». 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.2021/406.
Texte intégralJuliani, Arthur, Ahmed Khalifa, Vincent-Pierre Berges, Jonathan Harper, Ervin Teng, Hunter Henry, Adam Crespi, Julian Togelius et Danny Lange. « Obstacle Tower : A Generalization Challenge in Vision, Control, and Planning ». Dans Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California : International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/373.
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