Letteratura scientifica selezionata sul tema "Soft Actor-Critic"
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Articoli di riviste sul tema "Soft Actor-Critic":
Hyeon, Soo-Jong, Tae-Young Kang e Chang-Kyung Ryoo. "A Path Planning for Unmanned Aerial Vehicles Using SAC (Soft Actor Critic) Algorithm". Journal of Institute of Control, Robotics and Systems 28, n. 2 (28 febbraio 2022): 138–45. http://dx.doi.org/10.5302/j.icros.2022.21.0220.
Ding, Feng, Guanfeng Ma, Zhikui Chen, Jing Gao e Peng Li. "Averaged Soft Actor-Critic for Deep Reinforcement Learning". Complexity 2021 (1 aprile 2021): 1–16. http://dx.doi.org/10.1155/2021/6658724.
Qin, Chenjie, Lijun Zhang, Dawei Yin, Dezhong Peng e Yongzhong Zhuang. "Some effective tricks are used to improve Soft Actor Critic". Journal of Physics: Conference Series 2010, n. 1 (1 settembre 2021): 012061. http://dx.doi.org/10.1088/1742-6596/2010/1/012061.
Yang, Qisong, Thiago D. Simão, Simon H. Tindemans e Matthijs T. J. Spaan. "WCSAC: Worst-Case Soft Actor Critic for Safety-Constrained Reinforcement Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, n. 12 (18 maggio 2021): 10639–46. http://dx.doi.org/10.1609/aaai.v35i12.17272.
Wong, Ching-Chang, Shao-Yu Chien, Hsuan-Ming Feng e Hisasuki Aoyama. "Motion Planning for Dual-Arm Robot Based on Soft Actor-Critic". IEEE Access 9 (2021): 26871–85. http://dx.doi.org/10.1109/access.2021.3056903.
Wu, Xiongwei, Xiuhua Li, Jun Li, P. C. Ching, Victor C. M. Leung e H. Vincent Poor. "Caching Transient Content for IoT Sensing: Multi-Agent Soft Actor-Critic". IEEE Transactions on Communications 69, n. 9 (settembre 2021): 5886–901. http://dx.doi.org/10.1109/tcomm.2021.3086535.
Ali, Hamid, Hammad Majeed, Imran Usman e Khaled A. Almejalli. "Reducing Entropy Overestimation in Soft Actor Critic Using Dual Policy Network". Wireless Communications and Mobile Computing 2021 (10 giugno 2021): 1–13. http://dx.doi.org/10.1155/2021/9920591.
Sola, Yoann, Gilles Le Chenadec e Benoit Clement. "Simultaneous Control and Guidance of an AUV Based on Soft Actor–Critic". Sensors 22, n. 16 (14 agosto 2022): 6072. http://dx.doi.org/10.3390/s22166072.
Yu, Xin, Yushan Sun, Xiangbin Wang e Guocheng Zhang. "End-to-End AUV Motion Planning Method Based on Soft Actor-Critic". Sensors 21, n. 17 (1 settembre 2021): 5893. http://dx.doi.org/10.3390/s21175893.
Al Younes, Younes Al, e Martin Barczyk. "Adaptive Nonlinear Model Predictive Horizon Using Deep Reinforcement Learning for Optimal Trajectory Planning". Drones 6, n. 11 (27 ottobre 2022): 323. http://dx.doi.org/10.3390/drones6110323.
Tesi sul tema "Soft Actor-Critic":
Sola, Yoann. "Contributions to the development of deep reinforcement learning-based controllers for AUV". Thesis, Brest, École nationale supérieure de techniques avancées Bretagne, 2021. http://www.theses.fr/2021ENTA0015.
The marine environment is a very hostile setting for robotics. It is strongly unstructured, very uncertain and includes a lot of external disturbances which cannot be easily predicted or modelled. In this work, we will try to control an autonomous underwater vehicle (AUV) in order to perform a waypoint tracking task, using a machine learning-based controller. Machine learning allowed to make impressive progress in a lot of different domain in the recent years, and the subfield of deep reinforcement learning managed to design several algorithms very suitable for the continuous control of dynamical systems. We chose to implement the Soft Actor-Critic (SAC) algorithm, an entropy-regularized deep reinforcement learning algorithm allowing to fulfill a learning task and to encourage the exploration of the environment simultaneously. We compared a SAC-based controller with a Proportional-Integral-Derivative (PID) controller on a waypoint tracking task and using specific performance metrics. All the tests were performed in simulation thanks to the use of the UUV Simulator. We decided to apply these two controllers to the RexROV 2, a six degrees of freedom cube-shaped remotely operated underwater vehicle (ROV) converted in an AUV. Thanks to these tests, we managed to propose several interesting contributions such as making the SAC achieve an end-to-end control of the AUV, outperforming the PID controller in terms of energy saving, and reducing the amount of information needed by the SAC algorithm. Moreover we propose a methodology for the training of deep reinforcement learning algorithms on control tasks, as well as a discussion about the absence of guidance algorithms for our end-to-end AUV controller
Capitoli di libri sul tema "Soft Actor-Critic":
Chen, Tao, Xingxing Ma, Shixun You e Xiaoli Zhang. "Soft Actor-Critic-Based Continuous Control Optimization for Moving Target Tracking". In Lecture Notes in Computer Science, 630–41. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34110-7_53.
Huang, Shiyu, Bin Wang, Hang Su, Dong Li, Jianye Hao, Jun Zhu e Ting Chen. "Off-Policy Training for Truncated TD($$\lambda $$) Boosted Soft Actor-Critic". In PRICAI 2021: Trends in Artificial Intelligence, 46–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89370-5_4.
Liu, Pengbo, Shuxin Ge, Xiaobo Zhou, Chaokun Zhang e Keqiu Li. "Soft Actor-Critic-Based DAG Tasks Offloading in Multi-access Edge Computing with Inter-user Cooperation". In Algorithms and Architectures for Parallel Processing, 313–27. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95391-1_20.
Lin, Fangze, Wei Ning e Zhengrong Zou. "Fed-MT-ISAC: Federated Multi-task Inverse Soft Actor-Critic for Human-Like NPCs in the Metaverse Games". In Intelligent Computing Methodologies, 492–503. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13832-4_41.
Atti di convegni sul tema "Soft Actor-Critic":
Pu, Yuan, Shaochen Wang, Xin Yao e Bin Li. "Latent Context Based Soft Actor-Critic". In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207008.
Fan, Ting-Han, e Yubo Wang. "Soft Actor-Critic With Integer Actions". In 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867395.
Nishio, Daichi, Toi Tsuneda, Daiki Kuyoshi e Satoshi Yamane. "Discriminator Soft Actor Critic without Extrinsic Rewards". In 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE). IEEE, 2020. http://dx.doi.org/10.1109/gcce50665.2020.9292009.
Tan, Mingxi, Andong Tian e Ludovic Denoyer. "Regularized Soft Actor-Critic for Behavior Transfer Learning". In 2022 IEEE Conference on Games (CoG). IEEE, 2022. http://dx.doi.org/10.1109/cog51982.2022.9893655.
Savari, Maryam, e Yoonsuck Choe. "Online Virtual Training in Soft Actor-Critic for Autonomous Driving". In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533791.
Rao, Ning, Hua Xu, Balin Song e Yunhao Shi. "Soft Actor-Critic Deep Reinforcement Learning Based Interference Resource Allocation". In ICCAI '21: 2021 7th International Conference on Computing and Artificial Intelligence. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3467707.3467766.
Choi, Jinyoung, Christopher Dance, Jung-Eun Kim, Seulbin Hwang e Kyung-Sik Park. "Risk-Conditioned Distributional Soft Actor-Critic for Risk-Sensitive Navigation". In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9560962.
Nematollahi, Iman, Erick Rosete-Beas, Adrian Rpfer, Tim Welschehold, Abhinav Valada e Wolfram Burgard. "Robot Skill Adaptation via Soft Actor-Critic Gaussian Mixture Models". In 2022 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2022. http://dx.doi.org/10.1109/icra46639.2022.9811770.
Li, Dingcheng, Xu Li, Jun Wang e Ping Li. "Video Recommendation with Multi-gate Mixture of Experts Soft Actor Critic". In SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3397271.3401238.
Ren, Yangang, Jingliang Duan, Shengbo Eben Li, Yang Guan e Qi Sun. "Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic". In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2020. http://dx.doi.org/10.1109/itsc45102.2020.9294300.