کتاب صوتی بررسی عمق تئوری همگرایی در یادگیری تقویتی چندعامله

🎓 دوره آموزشی جامع

📚 اطلاعات دوره

عنوان دوره: دوره بررسی عمق تئوری همگرایی در یادگیری تقویتی چندعامله

موضوع کلی: یادگیری تقویتی چندعامله (MARL)

موضوع میانی: آنالیز همگرایی (Convergence Analysis)

📋 سرفصل‌های دوره

• 1. مبانی یادگیری تقویتی چندعامله
• 2. مدل‌های عامل در محیط‌های پویا
• 3. فرایندهای تصمیم‌گیری مارکوف تعمیم‌یافته
• 4. تابع ارزش و تابع بهینه در بازی‌های تکراری
• 5. همگرایی در بازی‌های تنسور
• 6. بازی‌های تنسور و نظریه پتانسیل
• 7. رویکردهای مبتنی بر تئوری بازی برای یادگیری تقویتی
• 8. تعادل نش در بازی‌های چندعامله
• 9. همگرایی به تعادل نش با یادگیری
• 10. الگوریتم‌های یادگیری تطبیقی در بازی‌های غیرهمکارانه
• 11. یادگیری مبتنی بر عامل در محیط‌های با اطلاعات ناقص
• 12. مدل‌سازی محیط و عامل‌های دیگر
• 13. یادگیری تقویتی مبتنی بر مدل برای چندعامله
• 14. یادگیری تقویتی بدون مدل برای چندعامله
• 15. الگوریتم‌های Q-learning برای چندعامله
• 16. SARSA برای محیط‌های چندعامله
• 17. Deep Q-Networks در یادگیری تقویتی چندعامله
• 18. Actor-Critic methods for Multi-Agent RL
• 19. Policy Gradient methods for Multi-Agent RL
• 20. Proximal Policy Optimization for Multi-Agent Systems
• 21. Multi-Agent Deep Deterministic Policy Gradient (MADDPG)
• 22. Counterfactual Multi-Agent Policy Gradients (COMA)
• 23. Learning Cooperative Policies in Multi-Agent Systems
• 24. Learning Competitive Policies in Multi-Agent Systems
• 25. Mixed Cooperative-Competitive Multi-Agent Learning
• 26. Centralized Training with Decentralized Execution (CTDE)
• 27. Value Decomposition Networks (VDN)
• 28. QMIX: Monotonic Value Function Factorisation
• 29. DIAL: Differentiable Inter-Agent Learning
• 30. CommNet: Communication Neural Networks
• 31. Gated Graph Neural Networks for Agent Communication
• 32. Learning with Communication Protocols
• 33. Emergent Communication in Multi-Agent Systems
• 34. Robustness of Multi-Agent RL to Agent Failures
• 35. Adversarial Attacks on Multi-Agent RL Policies
• 36. Safe Multi-Agent Reinforcement Learning
• 37. Explainable AI in Multi-Agent Systems
• 38. Fairness in Multi-Agent Decision Making
• 39. Game Theory Foundations for Multi-Agent Systems
• 40. Stochastic Games and their Applications
• 41. Cooperative Game Theory and Bargaining
• 42. Bargaining Solutions in Cooperative Games
• 43. Coalitional Game Theory
• 44. Mechanism Design for Multi-Agent Systems
• 45. Algorithmic Game Theory
• 46. Algorithmic Foundations of Social Choice
• 47. Information Design in Multi-Agent Settings
• 48. Learning in Repeated Games
• 49. Learning in Extensive-Form Games
• 50. Learning in Differential Games
• 51. Computational Complexity of Game Theory Problems
• 52. Approximation Algorithms for Game Theory
• 53. Online Learning in Game Theory
• 54. Learning from Expert Advice in Games
• 55. Reinforcement Learning for Mechanism Design
• 56. Fair Resource Allocation in Multi-Agent Systems
• 57. Distributed Optimization in Multi-Agent Systems
• 58. Consensus Algorithms in Multi-Agent Systems
• 59. Multi-Agent Pathfinding
• 60. Multi-Agent Coordination and Control
• 61. Multi-Agent Task Allocation
• 62. Multi-Agent Robot Swarms
• 63. Multi-Agent Simulation Environments
• 64. Evaluation Metrics for Multi-Agent RL
• 65. Benchmarking Multi-Agent RL Algorithms
• 66. Transfer Learning in Multi-Agent RL
• 67. Meta-Learning for Multi-Agent Systems
• 68. Lifelong Learning in Multi-Agent Environments
• 69. Multi-Agent Reinforcement Learning for Robotics
• 70. Multi-Agent Reinforcement Learning for Autonomous Driving
• 71. Multi-Agent Reinforcement Learning for Financial Markets
• 72. Multi-Agent Reinforcement Learning for Network Routing
• 73. Multi-Agent Reinforcement Learning for Smart Grids
• 74. Multi-Agent Reinforcement Learning for Healthcare
• 75. Multi-Agent Reinforcement Learning for Education
• 76. Multi-Agent Reinforcement Learning for Recommender Systems
• 77. Multi-Agent Reinforcement Learning for Cybersecurity
• 78. Multi-Agent Reinforcement Learning for Natural Language Processing
• 79. Multi-Agent Reinforcement Learning for Computer Vision
• 80. Ethical Considerations in Multi-Agent Systems
• 81. Societal Impact of Multi-Agent RL
• 82. Future Directions in Multi-Agent RL Research
• 83. Advanced Topics in Cooperative Multi-Agent RL
• 84. Advanced Topics in Competitive Multi-Agent RL
• 85. Advanced Topics in Mixed Multi-Agent RL
• 86. Advanced Topics in Communication Learning
• 87. Advanced Topics in Explainable Multi-Agent RL
• 88. Advanced Topics in Safe Multi-Agent RL
• 89. Advanced Topics in Multi-Agent Game Theory
• 90. Advanced Topics in Mechanism Design for Multi-Agent Systems
• 91. Advanced Topics in Distributed Multi-Agent Learning
• 92. Advanced Topics in Multi-Agent Coordination Strategies
• 93. Advanced Topics in Applications of Multi-Agent RL
• 94. Advanced Topics in Evaluation and Benchmarking of Multi-Agent RL