Littérature scientifique sur le sujet « Soft Actor-Critic »
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Articles de revues sur le sujet "Soft Actor-Critic":
Hyeon, Soo-Jong, Tae-Young Kang et 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, no 2 (28 février 2022) : 138–45. http://dx.doi.org/10.5302/j.icros.2022.21.0220.
Ding, Feng, Guanfeng Ma, Zhikui Chen, Jing Gao et Peng Li. « Averaged Soft Actor-Critic for Deep Reinforcement Learning ». Complexity 2021 (1 avril 2021) : 1–16. http://dx.doi.org/10.1155/2021/6658724.
Qin, Chenjie, Lijun Zhang, Dawei Yin, Dezhong Peng et Yongzhong Zhuang. « Some effective tricks are used to improve Soft Actor Critic ». Journal of Physics : Conference Series 2010, no 1 (1 septembre 2021) : 012061. http://dx.doi.org/10.1088/1742-6596/2010/1/012061.
Yang, Qisong, Thiago D. Simão, Simon H. Tindemans et Matthijs T. J. Spaan. « WCSAC : Worst-Case Soft Actor Critic for Safety-Constrained Reinforcement Learning ». Proceedings of the AAAI Conference on Artificial Intelligence 35, no 12 (18 mai 2021) : 10639–46. http://dx.doi.org/10.1609/aaai.v35i12.17272.
Wong, Ching-Chang, Shao-Yu Chien, Hsuan-Ming Feng et 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 et H. Vincent Poor. « Caching Transient Content for IoT Sensing : Multi-Agent Soft Actor-Critic ». IEEE Transactions on Communications 69, no 9 (septembre 2021) : 5886–901. http://dx.doi.org/10.1109/tcomm.2021.3086535.
Ali, Hamid, Hammad Majeed, Imran Usman et Khaled A. Almejalli. « Reducing Entropy Overestimation in Soft Actor Critic Using Dual Policy Network ». Wireless Communications and Mobile Computing 2021 (10 juin 2021) : 1–13. http://dx.doi.org/10.1155/2021/9920591.
Sola, Yoann, Gilles Le Chenadec et Benoit Clement. « Simultaneous Control and Guidance of an AUV Based on Soft Actor–Critic ». Sensors 22, no 16 (14 août 2022) : 6072. http://dx.doi.org/10.3390/s22166072.
Yu, Xin, Yushan Sun, Xiangbin Wang et Guocheng Zhang. « End-to-End AUV Motion Planning Method Based on Soft Actor-Critic ». Sensors 21, no 17 (1 septembre 2021) : 5893. http://dx.doi.org/10.3390/s21175893.
Al Younes, Younes Al, et Martin Barczyk. « Adaptive Nonlinear Model Predictive Horizon Using Deep Reinforcement Learning for Optimal Trajectory Planning ». Drones 6, no 11 (27 octobre 2022) : 323. http://dx.doi.org/10.3390/drones6110323.
Thèses sur le sujet "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
Chapitres de livres sur le sujet "Soft Actor-Critic":
Chen, Tao, Xingxing Ma, Shixun You et Xiaoli Zhang. « Soft Actor-Critic-Based Continuous Control Optimization for Moving Target Tracking ». Dans 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 et Ting Chen. « Off-Policy Training for Truncated TD($$\lambda $$) Boosted Soft Actor-Critic ». Dans 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 et Keqiu Li. « Soft Actor-Critic-Based DAG Tasks Offloading in Multi-access Edge Computing with Inter-user Cooperation ». Dans 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 et Zhengrong Zou. « Fed-MT-ISAC : Federated Multi-task Inverse Soft Actor-Critic for Human-Like NPCs in the Metaverse Games ». Dans Intelligent Computing Methodologies, 492–503. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13832-4_41.
Actes de conférences sur le sujet "Soft Actor-Critic":
Pu, Yuan, Shaochen Wang, Xin Yao et Bin Li. « Latent Context Based Soft Actor-Critic ». Dans 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207008.
Fan, Ting-Han, et Yubo Wang. « Soft Actor-Critic With Integer Actions ». Dans 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867395.
Nishio, Daichi, Toi Tsuneda, Daiki Kuyoshi et Satoshi Yamane. « Discriminator Soft Actor Critic without Extrinsic Rewards ». Dans 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE). IEEE, 2020. http://dx.doi.org/10.1109/gcce50665.2020.9292009.
Tan, Mingxi, Andong Tian et Ludovic Denoyer. « Regularized Soft Actor-Critic for Behavior Transfer Learning ». Dans 2022 IEEE Conference on Games (CoG). IEEE, 2022. http://dx.doi.org/10.1109/cog51982.2022.9893655.
Savari, Maryam, et Yoonsuck Choe. « Online Virtual Training in Soft Actor-Critic for Autonomous Driving ». Dans 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 et Yunhao Shi. « Soft Actor-Critic Deep Reinforcement Learning Based Interference Resource Allocation ». Dans 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 et Kyung-Sik Park. « Risk-Conditioned Distributional Soft Actor-Critic for Risk-Sensitive Navigation ». Dans 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 et Wolfram Burgard. « Robot Skill Adaptation via Soft Actor-Critic Gaussian Mixture Models ». Dans 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 et Ping Li. « Video Recommendation with Multi-gate Mixture of Experts Soft Actor Critic ». Dans 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 et Qi Sun. « Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic ». Dans 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2020. http://dx.doi.org/10.1109/itsc45102.2020.9294300.