دوره یادگیری تقویتی عمیق برای ادغام و هماهنگی سیستم‌های حمل و نقل هوشمند ملی همراه با فلش‌کارت آموزشی — ارائه روی فلش مموری

🎓 دوره آموزشی جامع

📚 اطلاعات دوره

عنوان دوره: دوره یادگیری تقویتی عمیق برای ادغام و هماهنگی سیستم‌های حمل و نقل هوشمند ملی

موضوع کلی: یادگیری تقویتی چندعامله (MARL)

موضوع میانی: یادگیری تقویتی چندعامله برای بهینه‌سازی سیستم‌های مدیریت حمل و نقل هوشمند در مقیاس ملی با استفاده از الگوریتم‌های یادگیری تقویتی عمیق

📋 سرفصل‌های دوره

• 1. مبانی یادگیری تقویتی
• 2. مفهوم پاداش و جریمه در یادگیری تقویتی
• 3. یادگیری تقویتی مبتنی بر ارزش
• 4. یادگیری تقویتی مبتنی بر سیاست
• 5. الگوریتم‌های Q-Learning
• 6. الگوریتم‌های SARSA
• 7. یادگیری تقویتی عمیق
• 8. شبکه‌های عصبی کانولوشنی در یادگیری تقویتی
• 9. شبکه‌های عصبی بازگشتی در یادگیری تقویتی
• 10. Deep Q-Networks (DQN)
• 11. Double DQN
• 12. Dueling DQN
• 13. Prioritized Experience Replay
• 14. Distributional DQN
• 15. Rainbow DQN
• 16. Policy Gradient Methods
• 17. REINFORCE
• 18. Actor-Critic Methods
• 19. Asynchronous Advantage Actor-Critic (A3C)
• 20. Deep Deterministic Policy Gradient (DDPG)
• 21. Trust Region Policy Optimization (TRPO)
• 22. Proximal Policy Optimization (PPO)
• 23. Multi-Agent Reinforcement Learning (MARL)
• 24. Cooperative MARL
• 25. Competitive MARL
• 26. Hierarchical Reinforcement Learning (HRL)
• 27. State Representation Learning
• 28. Feature Extraction for RL
• 29. Transfer Learning in RL
• 30. Meta-Learning for RL
• 31. Reinforcement Learning for Control Systems
• 32. Reinforcement Learning for Robotics
• 33. Reinforcement Learning for Game Playing
• 34. Reinforcement Learning for Finance
• 35. Reinforcement Learning for Healthcare
• 36. Reinforcement Learning for Natural Language Processing
• 37. Reinforcement Learning for Computer Vision
• 38. Reinforcement Learning for Recommendation Systems
• 39. Reinforcement Learning for Traffic Signal Control
• 40. Reinforcement Learning for Autonomous Driving
• 41. Reinforcement Learning for Intelligent Transportation Systems (ITS)
• 42. System Integration in ITS
• 43. Data Fusion for ITS
• 44. Communication Protocols in ITS
• 45. Cybersecurity in ITS
• 46. Ethical Considerations in ITS
• 47. Privacy in ITS
• 48. Regulatory Frameworks for ITS in Iran
• 49. Standards for ITS in Iran
• 50. Role of AI in ITS
• 51. Machine Learning in ITS
• 52. Deep Learning in ITS
• 53. Big Data Analytics in ITS
• 54. Cloud Computing in ITS
• 55. Edge Computing in ITS
• 56. Internet of Things (IoT) in ITS
• 57. Sensors and Actuators in ITS
• 58. Modeling of Transportation Networks
• 59. Simulation of Transportation Systems
• 60. Optimization of Traffic Flow
• 61. Demand Prediction in Transportation
• 62. Supply Chain Optimization in Transportation
• 63. Logistics Management
• 64. Freight Transportation Optimization
• 65. Passenger Transportation Optimization
• 66. Public Transportation Management
• 67. Smart Mobility Services
• 68. Vehicle-to-Everything (V2X) Communication
• 69. Connected Vehicles
• 70. Autonomous Vehicles
• 71. Traffic Management Centers
• 72. Emergency Response Systems
• 73. Environmental Impact of Transportation
• 74. Sustainable Transportation Solutions
• 75. Energy Efficiency in Transportation
• 76. Electric Vehicle Integration
• 77. Smart Parking Systems
• 78. Infrastructure Planning for ITS
• 79. Maintenance and Upgrades of ITS
• 80. Performance Evaluation of ITS
• 81. Case Studies of ITS Implementation in Iran
• 82. Future Trends in ITS
• 83. Challenges in ITS Deployment
• 84. Policy Recommendations for ITS
• 85. Economic Benefits of ITS
• 86. Social Impact of ITS
• 87. Human Factors in ITS
• 88. User Acceptance of ITS
• 89. Interoperability of ITS Components
• 90. Scalability of ITS Solutions
• 91. Resilience of ITS Systems
• 92. Disaster Management and ITS
• 93. Urban Planning and ITS
• 94. Rural Transportation and ITS
• 95. International Cooperation in ITS
• 96. Knowledge Transfer in ITS
• 97. Capacity Building for ITS Professionals
• 98. Research and Development in ITS
• 99. Innovation in ITS
• 100. The Role of Government in ITS Development