Summary of Narrowing the Gap Between Adversarial and Stochastic Mdps Via Policy Optimization, by Daniil Tiapkin (cmap et al.
Narrowing the Gap between Adversarial and Stochastic MDPs via Policy Optimization
by Daniil Tiapkin, Evgenii Chzhen, Gilles Stoltz
First submitted to arxiv on: 8 Jul 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 The proposed algorithm, APO-MVP, tackles the problem of learning in adversarial Markov decision processes with an oblivious adversary. In this setting, the agent interacts with an environment during multiple episodes, each consisting of stages, and is evaluated based on a reward function revealed only at the end of each episode. The algorithm achieves a regret bound that improves upon previous results by bridging the gap between adversarial and stochastic MDPs. This is achieved through policy optimization using dynamic programming and online linear optimization strategies, making it easy to implement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop an algorithm called APO-MVP that helps agents learn in complex situations where they might not know what will happen next. The algorithm works by breaking down the problem into smaller parts and solving them one at a time. This makes it easier for the agent to learn and make good decisions. |
Keywords
* Artificial intelligence * Optimization
