Computer Applications and Software, Volume. 42, Issue 4, 279(2025)
FAST TRAINING METHOD OF DEEP REINFORCEMENT LEARNING DIMENSIONALITY REDUCTION FOR MECHANICAL ARM
[1] [1] Ibarz J, Tan J, Finn C, et al. How to train your robot with deep reinforcement learning: lessons we have learned[J]. The International Journal of Robotics Research, 2021, 40: 698-721.
[2] [2] Kalashnikov D, Irpan A, Pastor P, et al. QT-Opt: Scalable deep reinforcement learning for vision-based robotic manipulation[C]//2nd Conference on Robot Learning (CoRL 2018), 2018.
[3] [3] Zhang J, Zhang W, Song R, et al. Grasp for stacking via deep reinforcement learning[C]//2020 IEEE International Conference on Robotics and Automation, 2020: 2543-2549.
[4] [4] Wang C, Zhang Q, Tian Q, et al. Multi-task reinforcement learning based mobile manipulation control for dynamic object tracking and grasping[C]//2022 7th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), 2022.
[5] [5] Wu B, Akinola I, Allen P K. Pixel-attentive policy gradient for multi-fingered grasping in cluttered scenes[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019: 1789-1796.
[6] [6] Wen S, Chen J, Shen W, et al. Path planning of humanoid arm based on deep deterministic policy gradient[C]//2018 IEEE International Conference on Robotics and Biomimetics, 2018: 1755-1760.
[7] [7] Liu C, Gao J, Bi Y, et al. A multitasking-oriented robot arm motion planning scheme based on deep reinforcement learning and twin synchro-control[J]. Sensors, 2020, 20 (12): 3515.
[8] [8] Chen P, Lu W. Deep reinforcement learning based moving object grasping[J]. Information Sciences, 2021, 565: 62-76.
[9] [9] Lillicrap T P, Hunt J J, Pritzel A, et al. Continuous control with deep reinforcement learning[C]//International Symposium on Experimental Robotics, 2016.
[10] [10] Chebotar Y, Handa A, Makoviychuk V, et al. Closing the sim-to-real loop: Adapting simulation randomization with real world experience[C]//2019 International Conference on Robotics and Automation, 2019: 8973-8979.
[11] [11] Tan J, Zhang T, Coumans E, et al. Sim-to-real: Learning agile locomotion for quadruped robots[C]//Robotics: Science and Systems, 2018.
[12] [12] Lin Y, Huang J, Zimmer M, et al. Invariant transform experience replay: Data augmentation for deep reinforcement learning[J]. IEEE Robotics and Automation Letters, 2020 (99): 6615-6622.
[13] [13] Hou Y, Liu L, Wei Q, et al. A novel DDPG method with prioritized experience replay[C]//IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2017: 316-321.
[14] [14] Scfaul T, Quan J, Antonoglou I, et al. Prioritized experience replay[C]//International Symposium on Experimental Robotics, 2016.
[15] [15] Fujimoto S, Hoof H V, Meger D. Addressing function approximation error in actor-critic methods[C]//35th International Conference on Machine Learning (ICML), 2018.
[16] [16] Li Z, Hou X. Mixing update q-value for deep reinforcement learning[C]//2019 International Joint Conference on Neural Networks (IJCNN), 2019.
[17] [17] Zhang Y, Clavera I, Tsai B, et al. Asynchronous methods for model-based reinforcement learning[C]//Proceedings of the 33rd International Conference on Machine Learning, 2016.
Get Citation
Copy Citation Text
Wang Min, Wang Zan, Li Shen, Chen Lijia, Fan Xianbojun, Wang Chenlu, Liu Mingguo. FAST TRAINING METHOD OF DEEP REINFORCEMENT LEARNING DIMENSIONALITY REDUCTION FOR MECHANICAL ARM[J]. Computer Applications and Software, 2025, 42(4): 279
Category:
Received: Jan. 18, 2022
Accepted: Aug. 25, 2025
Published Online: Aug. 25, 2025
The Author Email:
