Summary of On Value Iteration Convergence in Connected Mdps, by Arsenii Mustafin et al.
On Value Iteration Convergence in Connected MDPs
by Arsenii Mustafin, Alex Olshevsky, Ioannis Ch. Paschalidis
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning research paper proposes a novel framework for ensuring the convergence of various value iteration algorithms. The study demonstrates that when an MDP (Markov Decision Process) has a unique optimal policy and ergodic associated transition matrix, the Value Iteration algorithm converges at a geometric rate exceeding the discount factor γ, applicable to both discounted and average-reward criteria. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores how MDPs with specific properties can improve the convergence of value iteration algorithms. By combining concepts from Markov decision processes and value iteration methods, the study shows that certain conditions in an MDP can lead to faster convergence rates. |
Keywords
* Artificial intelligence * Machine learning
