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