Gotowa bibliografia na temat „Sparsely rewarded environments”
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Artykuły w czasopismach na temat "Sparsely rewarded environments"
Dubey, Rachit, Thomas L. Griffiths i Peter Dayan. "The pursuit of happiness: A reinforcement learning perspective on habituation and comparisons". PLOS Computational Biology 18, nr 8 (4.08.2022): e1010316. http://dx.doi.org/10.1371/journal.pcbi.1010316.
Pełny tekst źródłaShi, Xiaoping, Shiqi Zou, Shenmin Song i 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, nr 5 (22.04.2021): 10043–61. http://dx.doi.org/10.3233/jifs-202679.
Pełny tekst źródłaSakamoto, Yuma, i Kentarou Kurashige. "Self-Generating Evaluations for Robot’s Autonomy Based on Sensor Input". Machines 11, nr 9 (6.09.2023): 892. http://dx.doi.org/10.3390/machines11090892.
Pełny tekst źródłaParisi, Simone, Davide Tateo, Maximilian Hensel, Carlo D’Eramo, Jan Peters i Joni Pajarinen. "Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning". Algorithms 15, nr 3 (28.02.2022): 81. http://dx.doi.org/10.3390/a15030081.
Pełny tekst źródłaMguni, David, Taher Jafferjee, Jianhong Wang, Nicolas Perez-Nieves, Wenbin Song, Feifei Tong, Matthew Taylor i in. "Learning to Shape Rewards Using a Game of Two Partners". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 10 (26.06.2023): 11604–12. http://dx.doi.org/10.1609/aaai.v37i10.26371.
Pełny tekst źródłaForbes, Grant C., i David L. Roberts. "Potential-Based Reward Shaping for Intrinsic Motivation (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 21 (24.03.2024): 23488–89. http://dx.doi.org/10.1609/aaai.v38i21.30441.
Pełny tekst źródłaXu, Pei, Junge Zhang, Qiyue Yin, Chao Yu, Yaodong Yang i Kaiqi Huang. "Subspace-Aware Exploration for Sparse-Reward Multi-Agent Tasks". Proceedings of the AAAI Conference on Artificial Intelligence 37, nr 10 (26.06.2023): 11717–25. http://dx.doi.org/10.1609/aaai.v37i10.26384.
Pełny tekst źródłaKubovčík, Martin, Iveta Dirgová Luptáková i Jiří Pospíchal. "Signal Novelty Detection as an Intrinsic Reward for Robotics". Sensors 23, nr 8 (14.04.2023): 3985. http://dx.doi.org/10.3390/s23083985.
Pełny tekst źródłaCatacora Ocana, Jim Martin, Roberto Capobianco i Daniele Nardi. "An Overview of Environmental Features that Impact Deep Reinforcement Learning in Sparse-Reward Domains". Journal of Artificial Intelligence Research 76 (26.04.2023): 1181–218. http://dx.doi.org/10.1613/jair.1.14390.
Pełny tekst źródłaZhou, Xiao, Song Zhou, Xingang Mou i Yi He. "Multirobot Collaborative Pursuit Target Robot by Improved MADDPG". Computational Intelligence and Neuroscience 2022 (25.02.2022): 1–10. http://dx.doi.org/10.1155/2022/4757394.
Pełny tekst źródłaRozprawy doktorskie na temat "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.
Pełny tekst źródłaConventional 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, i 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.
Pełny tekst źródłaCzęści książek na temat "Sparsely rewarded environments"
Hensel, Maximilian. "Exploration Methods in Sparse Reward Environments". W 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.
Pełny tekst źródłaMoy, Glennn, i Slava Shekh. "Evolution Strategies for Sparse Reward Gridworld Environments". W 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.
Pełny tekst źródłaJeewa, Asad, Anban W. Pillay i Edgar Jembere. "Learning to Generalise in Sparse Reward Navigation Environments". W Artificial Intelligence Research, 85–100. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66151-9_6.
Pełny tekst źródłaChen, Zhongpeng, i Qiang Guan. "Continuous Exploration via Multiple Perspectives in Sparse Reward Environment". W Pattern Recognition and Computer Vision, 57–68. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8435-0_5.
Pełny tekst źródłaLe, Bang-Giang, Thi-Linh Hoang, Hai-Dang Kieu i Viet-Cuong Ta. "Structural and Compact Latent Representation Learning on Sparse Reward Environments". W Intelligent Information and Database Systems, 40–51. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5837-5_4.
Pełny tekst źródłaKang, Yongxin, Enmin Zhao, Yifan Zang, Kai Li i Junliang Xing. "Towards a Unified Benchmark for Reinforcement Learning in Sparse Reward Environments". W 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.
Pełny tekst źródłaLiu, Xi, Long Ma, Zhen Chen, Changgang Zheng, Ren Chen, Yong Liao i Shufan Yang. "A Novel State Space Exploration Method for the Sparse-Reward Reinforcement Learning Environment". W Artificial Intelligence XL, 216–21. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47994-6_18.
Pełny tekst źródłaXie, Zaipeng, Yufeng Zhang, Chentai Qiao i Sitong Shen. "IPERS: Individual Prioritized Experience Replay with Subgoals for Sparse Reward Multi-Agent Reinforcement Learning". W Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230586.
Pełny tekst źródłaShah, Syed Ihtesham Hussain, Antonio Coronato i Muddasar Naeem. "Inverse Reinforcement Learning Based Approach for Investigating Optimal Dynamic Treatment Regime". W Ambient Intelligence and Smart Environments. IOS Press, 2022. http://dx.doi.org/10.3233/aise220052.
Pełny tekst źródłaAbate, Alessandro, Yousif Almulla, James Fox, David Hyland i Michael Wooldridge. "Learning Task Automata for Reinforcement Learning Using Hidden Markov Models". W Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230247.
Pełny tekst źródłaStreszczenia konferencji na temat "Sparsely rewarded environments"
Camacho, Alberto, Rodrigo Toro Icarte, Toryn Q. Klassen, Richard Valenzano i Sheila A. McIlraith. "LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning". W 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.
Pełny tekst źródłaBougie, Nicolas, i Ryutaro Ichise. "Towards High-Level Intrinsic Exploration in Reinforcement Learning". W 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.
Pełny tekst źródłaWan, Shanchuan, Yujin Tang, Yingtao Tian i Tomoyuki Kaneko. "DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards". W 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.
Pełny tekst źródłaNoever, David, i Ryerson Burdick. "Puzzle Solving without Search or Human Knowledge: An Unnatural Language Approach". W 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.
Pełny tekst źródłaChatterjee, Palash, Ashutosh Chapagain, Weizhe Chen i Roni Khardon. "DiSProD: Differentiable Symbolic Propagation of Distributions for Planning". W 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.
Pełny tekst źródłaXu, Pei, Junge Zhang i Kaiqi Huang. "Exploration via Joint Policy Diversity for Sparse-Reward Multi-Agent Tasks". W 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.
Pełny tekst źródłaMemarian, Farzan, Wonjoon Goo, Rudolf Lioutikov, Scott Niekum i Ufuk Topcu. "Self-Supervised Online Reward Shaping in Sparse-Reward Environments". W 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2021. http://dx.doi.org/10.1109/iros51168.2021.9636020.
Pełny tekst źródłaLin, Xingyu, Pengsheng Guo, Carlos Florensa i David Held. "Adaptive Variance for Changing Sparse-Reward Environments". W 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8793650.
Pełny tekst źródłaSeurin, Mathieu, Florian Strub, Philippe Preux i Olivier Pietquin. "Don’t Do What Doesn’t Matter: Intrinsic Motivation with Action Usefulness". W 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.
Pełny tekst źródłaJuliani, Arthur, Ahmed Khalifa, Vincent-Pierre Berges, Jonathan Harper, Ervin Teng, Hunter Henry, Adam Crespi, Julian Togelius i Danny Lange. "Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning". W 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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