Learning Decentralized Policies in Multiagent Systems - How to Learn Efficiently

Learning Decentralized Policies in Multiagent Systems - How to Learn Efficiently

Simons Institute via YouTube Direct link

Convergence of gradient play?

17 of 18

17 of 18

Convergence of gradient play?

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Learning Decentralized Policies in Multiagent Systems - How to Learn Efficiently

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  1. 1 Intro
  2. 2 Opportunities and Challenges Decision-making
  3. 3 Learning (Data-driven decision-making) is a promis
  4. 4 Control of Networked Markov Decision Process
  5. 5 Examples of Systems with the local interact
  6. 6 Scalable RL for Network Systems
  7. 7 Review: Policy Gradient in the Full Information C
  8. 8 RL in the Network Setting
  9. 9 The Exponential Decay Property
  10. 10 Truncation of Q-function
  11. 11 Numerical results: Multi-Access Wireless Communic
  12. 12 Other (Multiagent) Learning Settings Decentralized Control
  13. 13 Optimality Guarantee
  14. 14 Optimization Landscape
  15. 15 Gradient play for identical interest case
  16. 16 General Stochastic Games
  17. 17 Convergence of gradient play?
  18. 18 Summary

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