Summary of Isoperimetry Is All We Need: Langevin Posterior Sampling For Rl with Sublinear Regret, by Emilio Jorge et al.
Isoperimetry is All We Need: Langevin Posterior Sampling for RL with Sublinear Regret
by Emilio Jorge, Christos Dimitrakakis, Debabrota Basu
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 This paper investigates how to design reinforcement learning (RL) algorithms that achieve sublinear regret in a wide range of distributions and models, moving beyond traditional assumptions like linear or RKHS models and Gaussian or log-concave posteriors. The authors focus on isoperimetric distributions satisfying the Log-Sobolev Inequality (LSI), which includes standard RL setups as well as non-log-concave and perturbed distributions. They propose two algorithms: Posterior Sampling-based RL (PSRL) and Langevin sampling-based LaPSRL, both of which achieve sublinear regret with order-optimal regret and subquadratic complexity per episode. The authors demonstrate the generality of their approach by deploying LaPSRL with a Langevin sampler, SARAH-LD, in various bandit and MDP environments, showing competitive performance with respect to baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computer programs learn from experience without getting stuck. Right now, many programs can only learn by following simple rules or patterns. The authors want to make them better at learning by looking at a special kind of math problem called the Log-Sobolev Inequality (LSI). They create two new ways for programs to learn: one uses something called posterior sampling and the other uses Langevin sampling. Both methods can help programs learn faster and make better decisions. The authors test their ideas in different situations, like playing games or making recommendations, and show that they work well. |
Keywords
» Artificial intelligence » Reinforcement learning
