Summary of Best-of-both-worlds Policy Optimization For Cmdps with Bandit Feedback, by Francesco Emanuele Stradi et al.
Best-of-Both-Worlds Policy Optimization for CMDPs with Bandit Feedback
by Francesco Emanuele Stradi, Anna Lunghi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
First submitted to arxiv on: 3 Oct 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 This paper addresses online learning in constrained Markov decision processes (CMDPs) with both stochastic and adversarial rewards and constraints. A previous algorithm by Stradi et al. (2024) achieved optimal regret and constraint violation bounds but had limitations, including requiring full feedback and relying on optimizing occupancy measures, which is inefficient. This paper presents the first best-of-both-worlds algorithm for CMDPs with bandit feedback, achieving () regret and constraint violation for stochastic constraints and () constraint violation and a fraction of optimal reward for adversarial constraints. The algorithm uses a policy optimization approach, which is more efficient than previous methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can learn to make good decisions online when there are rules or constraints that must be followed. Previously, an algorithm was developed that worked well but had some limitations. This new algorithm solves those problems and allows for faster learning while still following the rules. It’s a big improvement over what came before! |
Keywords
» Artificial intelligence » Online learning » Optimization
