{"id":62743,"date":"2025-04-12T13:25:35","date_gmt":"2025-04-12T13:25:35","guid":{"rendered":""},"modified":"2025-04-12T13:25:35","modified_gmt":"2025-04-12T13:25:35","slug":"%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-62743","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-62743\/","title":{"rendered":"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 Meta Sac-Lag: \u0628\u0647 \u0633\u0645\u062a \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062a\u0642\u0648\u06cc\u062a \u0627\u06cc\u0645\u0646 \u0642\u0627\u0628\u0644 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u062a\u0646\u0638\u06cc\u0645 Hyperparameter \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u0645\u062a\u0627\u06af\u0631\u0627\u0641\u06cc"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nMeta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning<\/td>\n<\/tr>\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 Meta Sac-Lag: \u0628\u0647 \u0633\u0645\u062a \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062a\u0642\u0648\u06cc\u062a \u0627\u06cc\u0645\u0646 \u0642\u0627\u0628\u0644 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u062a\u0646\u0638\u06cc\u0645 Hyperparameter \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u0645\u062a\u0627\u06af\u0631\u0627\u0641\u06cc<\/td>\n<\/tr>\n
\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\nHomayoun Honari, Amir Mehdi Soufi Enayati, Mehran Ghafarian Tamizi, Homayoun Najjaran<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nPDF<\/td>\n<\/tr>\n
\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u062a\u062d\u0648\u06cc\u0644\u06cc <\/td>\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u062a\u0631\u062c\u0645\u0647 \u0634\u062f\u0647 <\/td>\n\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\n<\/tr>\n
\u0646\u062d\u0648\u0647 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 <\/td>\n\u062f\u0648 \u062a\u0627 \u0633\u0647 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634 (\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u062f\u0627\u0646\u0644\u0648\u062f\u06cc)<\/td>\n<\/tr>\n
\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n10<\/td>\n<\/tr>\n
\u0644\u06cc\u0646\u06a9 \u062f\u0627\u0646\u0644\u0648\u062f \u0631\u0627\u06cc\u06af\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n\u062f\u0627\u0646\u0644\u0648\u062f \u0645\u0642\u0627\u0644\u0647<\/a><\/td>\n<\/tr>\n
\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a <\/td>\nMachine Learning,Artificial Intelligence,Robotics,Systems and Control,\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 , \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc , \u0631\u0648\u0628\u0627\u062a\u06cc\u06a9 , \u0633\u06cc\u0633\u062a\u0645 \u0648 \u06a9\u0646\u062a\u0631\u0644 ,<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a <\/td>\nSubmitted 15 August, 2024; originally announced August 2024. , Comments: Main text accepted to the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024, 10 pages, 4 figures, 3 tables<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 \u062f\u0631 15 \u0627\u0648\u062a 2024 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0627\u0648\u062a 2024 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f \u060c \u0646\u0638\u0631\u0627\u062a: \u0645\u062a\u0646 \u0627\u0635\u0644\u06cc \u067e\u0630\u06cc\u0631\u0641\u062a\u0647 \u0634\u062f\u0647 \u0628\u0631\u0627\u06cc \u06a9\u0646\u0641\u0631\u0627\u0646\u0633 \u0628\u06cc\u0646 \u0627\u0644\u0645\u0644\u0644\u06cc IEEE\/RSJ \u062f\u0631 \u0645\u0648\u0631\u062f \u0631\u0648\u0628\u0627\u062a \u0647\u0627 \u0648 \u0633\u06cc\u0633\u062a\u0645 \u0647\u0627\u06cc \u0647\u0648\u0634\u0645\u0646\u062f (IROS) 2024 \u060c 10 \u0635\u0641\u062d\u0647 \u060c 4 \u0634\u06a9\u0644 \u060c 3 \u062c\u062f\u0648\u0644<\/td>\n<\/tr>\n
\u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0628\u06cc\u0634\u062a\u0631 \u0627\u0632 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u067e\u0627\u06cc\u06af\u0627\u0647 \u0647\u0627\u06cc \u0639\u0644\u0645\u06cc <\/td>\n\n INSPIRE HEP<\/a>
\n
\n NASA ADS<\/a>
\n
\n Google Scholar<\/a>
\n
\n Semantic Scholar<\/a>
\n
\n arXiv<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n \r\n \r\n \r\n \r\n
\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n <\/tr>\r\n
\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n <\/tr>\r\n\t
\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 <\/td>\r\n \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n <\/tr>\r\n\t\t
\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 <\/td>\r\n \u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n <\/tr>\r\n<\/table>\r\n\r\n\n

\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n

Safe Reinforcement Learning (Safe RL) is one of the prevalently studied subcategories of trial-and-error-based methods with the intention to be deployed on real-world systems. In safe RL, the goal is to maximize reward performance while minimizing constraints, often achieved by setting bounds on constraint functions and utilizing the Lagrangian method. However, deploying Lagrangian-based safe RL in real-world scenarios is challenging due to the necessity of threshold fine-tuning, as imprecise adjustments may lead to suboptimal policy convergence. To mitigate this challenge, we propose a unified Lagrangian-based model-free architecture called Meta Soft Actor-Critic Lagrangian (Meta SAC-Lag). Meta SAC-Lag uses meta-gradient optimization to automatically update the safety-related hyperparameters. The proposed method is designed to address safe exploration and threshold adjustment with minimal hyperparameter tuning requirement. In our pipeline, the inner parameters are updated through the conventional formulation and the hyperparameters are adjusted using the meta-objectives which are defined based on the updated parameters. Our results show that the agent can reliably adjust the safety performance due to the relatively fast convergence rate of the safety threshold. We evaluate the performance of Meta SAC-Lag in five simulated environments against Lagrangian baselines, and the results demonstrate its capability to create synergy between parameters, yielding better or competitive results. Furthermore, we conduct a real-world experiment involving a robotic arm tasked with pouring coffee into a cup without spillage. Meta SAC-Lag is successfully trained to execute the task, while minimizing effort constraints.<\/p>\n

\u0686\u06a9\u06cc\u062f\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc)<\/h2>\n

\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062a\u0642\u0648\u06cc\u062a \u06a9\u0646\u0646\u062f\u0647 \u0627\u06cc\u0645\u0646 (RL \u0627\u06cc\u0645\u0646) \u06cc\u06a9\u06cc \u0627\u0632 \u0632\u06cc\u0631 \u0634\u0627\u062e\u0647 \u0647\u0627\u06cc \u0634\u06cc\u0648\u0639 \u0645\u0648\u0631\u062f \u0645\u0637\u0627\u0644\u0639\u0647 \u0631\u0648\u0634\u0647\u0627\u06cc \u0645\u062d\u0627\u06a9\u0645\u0647 \u0648 \u062e\u0637\u0627 \u0627\u0633\u062a \u06a9\u0647 \u0642\u0635\u062f \u062f\u0627\u0631\u062f \u062f\u0631 \u0633\u06cc\u0633\u062a\u0645 \u0647\u0627\u06cc \u062f\u0646\u06cc\u0627\u06cc \u0648\u0627\u0642\u0639\u06cc \u0645\u0633\u062a\u0642\u0631 \u0634\u0648\u062f.\u062f\u0631 RL \u0627\u06cc\u0645\u0646 \u060c \u0647\u062f\u0641 \u0627\u06cc\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0636\u0645\u0646 \u0628\u0647 \u062d\u062f\u0627\u0642\u0644 \u0631\u0633\u0627\u0646\u062f\u0646 \u0645\u062d\u062f\u0648\u062f\u06cc\u062a \u0647\u0627 \u060c \u0639\u0645\u0644\u06a9\u0631\u062f \u067e\u0627\u062f\u0627\u0634 \u0631\u0627 \u0628\u0647 \u062d\u062f\u0627\u06a9\u062b\u0631 \u0628\u0631\u0633\u0627\u0646\u062f \u060c \u06a9\u0647 \u0627\u063a\u0644\u0628 \u0628\u0627 \u062a\u0639\u06cc\u06cc\u0646 \u0645\u0631\u0632\u0647\u0627 \u0628\u0631 \u0639\u0645\u0644\u06a9\u0631\u062f\u0647\u0627\u06cc \u0645\u062d\u062f\u0648\u062f\u06cc\u062a \u0648 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0631\u0648\u0634 Lagrangian \u062d\u0627\u0635\u0644 \u0645\u06cc \u0634\u0648\u062f.\u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644 \u060c \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 RL \u0627\u06cc\u0645\u0646 \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u0644\u0627\u06af\u0631\u0627\u0646\u0698\u06cc \u062f\u0631 \u0633\u0646\u0627\u0631\u06cc\u0648\u0647\u0627\u06cc \u062f\u0646\u06cc\u0627\u06cc \u0648\u0627\u0642\u0639\u06cc \u0628\u0647 \u062f\u0644\u06cc\u0644 \u0636\u0631\u0648\u0631\u062a \u062a\u0646\u0638\u06cc\u0645 \u062f\u0642\u06cc\u0642 \u0622\u0633\u062a\u0627\u0646\u0647 \u0686\u0627\u0644\u0634 \u0628\u0631\u0627\u0646\u06af\u06cc\u0632 \u0627\u0633\u062a \u060c \u0632\u06cc\u0631\u0627 \u062a\u0646\u0638\u06cc\u0645\u0627\u062a \u0646\u0627\u062f\u0631\u0633\u062a \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0645\u0646\u062c\u0631 \u0628\u0647 \u0647\u0645\u06af\u0631\u0627\u06cc\u06cc \u0633\u06cc\u0627\u0633\u062a \u0647\u0627\u06cc \u0632\u06cc\u0631 \u062d\u062f \u0645\u062a\u0648\u0633\u0637 \u200b\u200b\u0634\u0648\u062f.\u0628\u0631\u0627\u06cc \u06a9\u0627\u0647\u0634 \u0627\u06cc\u0646 \u0686\u0627\u0644\u0634 \u060c \u0645\u0627 \u06cc\u06a9 \u0645\u0639\u0645\u0627\u0631\u06cc \u06cc\u06a9\u067e\u0627\u0631\u0686\u0647 \u0628\u062f\u0648\u0646 \u0645\u062f\u0644 \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 Lagrangian \u0628\u0647 \u0646\u0627\u0645 Meta Soft \u0628\u0627\u0632\u06cc\u06af\u0631-\u0627\u0646\u062a\u0642\u0627\u062f\u06cc Lagrangian (Meta Sac-Lag) \u067e\u06cc\u0634\u0646\u0647\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645.\u0645\u062a\u0627 \u0633\u0627\u06a9-\u0644\u0627\u06af \u0627\u0632 \u0628\u0647\u06cc\u0646\u0647 \u0633\u0627\u0632\u06cc \u0645\u062a\u0627 \u0628\u0631\u0627\u06cc \u0628\u0647 \u0631\u0648\u0632\u0631\u0633\u0627\u0646\u06cc \u0647\u0627\u06cc\u067e\u0631\u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0645\u0631\u062a\u0628\u0637 \u0628\u0627 \u0627\u06cc\u0645\u0646\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u062f.\u0631\u0648\u0634 \u067e\u06cc\u0634\u0646\u0647\u0627\u062f\u06cc \u0628\u0631\u0627\u06cc \u067e\u0631\u062f\u0627\u062e\u062a\u0646 \u0628\u0647 \u0627\u06a9\u062a\u0634\u0627\u0641 \u0627\u06cc\u0645\u0646 \u0648 \u062a\u0646\u0638\u06cc\u0645 \u0622\u0633\u062a\u0627\u0646\u0647 \u0628\u0627 \u062d\u062f\u0627\u0642\u0644 \u0646\u06cc\u0627\u0632 \u062a\u0646\u0638\u06cc\u0645 \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f hyperparameter \u0637\u0631\u0627\u062d\u06cc \u0634\u062f\u0647 \u0627\u0633\u062a.\u062f\u0631 \u062e\u0637 \u0644\u0648\u0644\u0647 \u0645\u0627 \u060c \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u062f\u0627\u062e\u0644\u06cc \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0641\u0631\u0645\u0648\u0644\u0627\u0633\u06cc\u0648\u0646 \u0645\u0639\u0645\u0648\u0644\u06cc \u0628\u0647 \u0631\u0648\u0632 \u0645\u06cc \u0634\u0648\u0646\u062f \u0648 Hyperparameters \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0645\u062a\u0627 \u0627\u0648\u0628\u0627\u062a\u0648\u0631\u0647\u0627 \u06a9\u0647 \u0628\u0631 \u0627\u0633\u0627\u0633 \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc \u0628\u0647 \u0631\u0648\u0632 \u0634\u062f\u0647 \u062a\u0639\u0631\u06cc\u0641 \u0645\u06cc \u0634\u0648\u0646\u062f \u060c \u062a\u0646\u0638\u06cc\u0645 \u0645\u06cc \u0634\u0648\u0646\u062f.\u0646\u062a\u0627\u06cc\u062c \u0645\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u06a9\u0647 \u0639\u0627\u0645\u0644 \u0628\u0647 \u062f\u0644\u06cc\u0644 \u0645\u06cc\u0632\u0627\u0646 \u0647\u0645\u06af\u0631\u0627\u06cc\u06cc \u0646\u0633\u0628\u062a\u0627\u064b \u0633\u0631\u06cc\u0639 \u0622\u0633\u062a\u0627\u0646\u0647 \u0627\u06cc\u0645\u0646\u06cc \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0639\u0645\u0644\u06a9\u0631\u062f \u0627\u06cc\u0645\u0646\u06cc \u0631\u0627 \u0628\u0627 \u0627\u0637\u0645\u06cc\u0646\u0627\u0646 \u062a\u0646\u0638\u06cc\u0645 \u06a9\u0646\u062f.\u0645\u0627 \u0639\u0645\u0644\u06a9\u0631\u062f \u0645\u062a\u0627 SAC-LAG \u0631\u0627 \u062f\u0631 \u067e\u0646\u062c \u0645\u062d\u06cc\u0637 \u0634\u0628\u06cc\u0647 \u0633\u0627\u0632\u06cc \u0634\u062f\u0647 \u062f\u0631 \u0628\u0631\u0627\u0628\u0631 \u062e\u0637\u0648\u0637 \u067e\u0627\u06cc\u0647 \u0644\u0627\u06af\u0631\u0627\u0646\u0698\u06cc \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u060c \u0648 \u0646\u062a\u0627\u06cc\u062c \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u062a\u0648\u0627\u0646\u0627\u06cc\u06cc \u0622\u0646 \u062f\u0631 \u0627\u06cc\u062c\u0627\u062f \u0647\u0645 \u0627\u0641\u0632\u0627\u06cc\u06cc \u0628\u06cc\u0646 \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627 \u060c \u0628\u0647 \u062f\u0633\u062a \u0622\u0648\u0631\u062f\u0646 \u0646\u062a\u0627\u06cc\u062c \u0628\u0647\u062a\u0631 \u06cc\u0627 \u0631\u0642\u0627\u0628\u062a\u06cc \u0627\u0633\u062a.\u0639\u0644\u0627\u0648\u0647 \u0628\u0631 \u0627\u06cc\u0646 \u060c \u0645\u0627 \u06cc\u06a9 \u0622\u0632\u0645\u0627\u06cc\u0634 \u062f\u0631 \u062f\u0646\u06cc\u0627\u06cc \u0648\u0627\u0642\u0639\u06cc \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc \u062f\u0647\u06cc\u0645 \u06a9\u0647 \u0634\u0627\u0645\u0644 \u06cc\u06a9 \u0628\u0627\u0632\u0648\u06cc \u0631\u0648\u0628\u0627\u062a\u06cc\u06a9 \u0627\u0633\u062a \u06a9\u0647 \u0648\u0638\u06cc\u0641\u0647 \u062f\u0627\u0631\u062f \u0642\u0647\u0648\u0647 \u0631\u0627 \u0628\u062f\u0648\u0646 \u0631\u06cc\u062e\u062a\u0646 \u06cc\u06a9 \u0641\u0646\u062c\u0627\u0646 \u0628\u0631\u06cc\u0632\u062f.\u0645\u062a\u0627 \u0633\u0627\u06a9-\u0644\u0627\u06af \u0628\u0627 \u0645\u0648\u0641\u0642\u06cc\u062a \u062f\u0631 \u0627\u062c\u0631\u0627\u06cc \u06a9\u0627\u0631 \u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f \u060c \u0636\u0645\u0646 \u0627\u06cc\u0646\u06a9\u0647 \u0645\u062d\u062f\u0648\u062f\u06cc\u062a \u0647\u0627\u06cc \u062a\u0644\u0627\u0634 \u0631\u0627 \u0628\u0647 \u062d\u062f\u0627\u0642\u0644 \u0645\u06cc \u0631\u0633\u0627\u0646\u062f.<\/p>\n\r\n\r\n \r\n \r\n \r\n \r\n
\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n <\/tr>\r\n
\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n <\/tr>\r\n\t
\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 <\/td>\r\n \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n <\/tr>\r\n\t\t
\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 <\/td>\r\n \u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n <\/tr>\r\n<\/table>\r\n\r\n\n","protected":false},"excerpt":{"rendered":"

\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc Meta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning \u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc \u062a\u0631\u062c\u0645\u0647 […]<\/p>\n","protected":false},"featured_media":27,"comment_status":"open","ping_status":"open","template":"","meta":{"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}}},"product_cat":[21],"product_tag":[],"class_list":{"0":"post-62743","1":"product","2":"type-product","3":"status-publish","4":"has-post-thumbnail","6":"product_cat-21","7":"pmpro-has-access","8":"desktop-align-left","9":"tablet-align-left","10":"mobile-align-left","12":"first","13":"instock","14":"downloadable","15":"shipping-taxable","16":"purchasable","17":"product-type-simple"},"yoast_head":"\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 Meta Sac-Lag: \u0628\u0647 \u0633\u0645\u062a \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062a\u0642\u0648\u06cc\u062a \u0627\u06cc\u0645\u0646 \u0642\u0627\u0628\u0644 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u062a\u0646\u0638\u06cc\u0645 Hyperparameter \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u0645\u062a\u0627\u06af\u0631\u0627\u0641\u06cc - \u0641\u0631\u0648\u0634\u06af\u0627\u0647 \u0627\u06a9\u0633\u067e\u0631\u0633<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/express24.ir\/d\/product\/\u062a\u0631\u062c\u0645\u0647-\u0641\u0627\u0631\u0633\u06cc-\u0645\u0642\u0627\u0644\u0647-62743\/\" \/>\n<meta property=\"og:locale\" content=\"fa_IR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 Meta Sac-Lag: \u0628\u0647 \u0633\u0645\u062a \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062a\u0642\u0648\u06cc\u062a \u0627\u06cc\u0645\u0646 \u0642\u0627\u0628\u0644 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u062a\u0646\u0638\u06cc\u0645 Hyperparameter \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u0645\u062a\u0627\u06af\u0631\u0627\u0641\u06cc - \u0641\u0631\u0648\u0634\u06af\u0627\u0647 \u0627\u06a9\u0633\u067e\u0631\u0633\" \/>\n<meta property=\"og:description\" content=\"\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc Meta SAC-Lag: Towards Deployable Safe Reinforcement Learning via MetaGradient-based Hyperparameter Tuning \u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc \u062a\u0631\u062c\u0645\u0647 […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/express24.ir\/d\/product\/\u062a\u0631\u062c\u0645\u0647-\u0641\u0627\u0631\u0633\u06cc-\u0645\u0642\u0627\u0644\u0647-62743\/\" \/>\n<meta property=\"og:site_name\" content=\"\u0641\u0631\u0648\u0634\u06af\u0627\u0647 \u0627\u06a9\u0633\u067e\u0631\u0633\" \/>\n<meta property=\"og:image\" content=\"https:\/\/express24.ir\/d\/wp-content\/uploads\/2024\/02\/Elsevier_logo_2019.svg_.png\" \/>\n\t<meta property=\"og:image:width\" content=\"440\" \/>\n\t<meta property=\"og:image:height\" content=\"486\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-62743\/\",\"url\":\"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-62743\/\",\"name\":\"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 Meta Sac-Lag: \u0628\u0647 \u0633\u0645\u062a \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u062a\u0642\u0648\u06cc\u062a \u0627\u06cc\u0645\u0646 \u0642\u0627\u0628\u0644 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u062a\u0646\u0638\u06cc\u0645 Hyperparameter \u0645\u0628\u062a\u0646\u06cc \u0628\u0631 \u0645\u062a\u0627\u06af\u0631\u0627\u0641\u06cc - 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