Summary of Rl in Latent Mdps Is Tractable: Online Guarantees Via Off-policy Evaluation, by Jeongyeol Kwon et al.
RL in Latent MDPs is Tractable: Online Guarantees via Off-Policy Evaluation
by Jeongyeol Kwon, Shie Mannor, Constantine Caramanis, Yonathan Efroni
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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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 novel sample-efficient algorithm is introduced for solving Latent Markov Decision Processes (LMDPs) without additional structural assumptions, resolving the long-standing challenge of provably matching the existing lower bound. The approach leverages a new perspective on off-policy evaluation guarantees and coverage coefficients in LMDPs, which has been overlooked in exploration problems with partially observed environments. Specifically, a novel off-policy evaluation lemma is established, along with a new coverage coefficient for LMDPs, enabling near-optimal guarantees of an optimistic exploration algorithm. This breakthrough can have far-reaching implications for interactive learning problems beyond LMDPs and partially observed environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in decision-making by introducing a new way to make choices when some information is missing or hidden. It’s called Latent Markov Decision Process (LMDP), and it’s really hard to solve without making mistakes. The researchers developed an algorithm that can do this efficiently, without needing any extra assumptions. This breakthrough can help with many real-world problems where we need to make decisions based on incomplete information. |
