Summary of The Number Of Trials Matters in Infinite-horizon General-utility Markov Decision Processes, by Pedro P. Santos et al.
The Number of Trials Matters in Infinite-Horizon General-Utility Markov Decision Processes
by Pedro P. Santos, Alberto Sardinha, Francisco S. Melo
First submitted to arxiv on: 23 Sep 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 paper introduces a generalized framework for Markov decision processes (GUMDPs) that considers objective functions dependent on state-action pair visitation frequencies induced by policies. The authors analyze the impact of the number of trials (randomly sampled trajectories) in infinite-horizon GUMDPs, showing it plays a crucial role and affects policy performance. They explore discounted and average GUMDPs, providing lower and upper bounds for policy evaluation mismatch between finite and infinite trials formulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how to evaluate the quality of decisions made by computers or AI systems when faced with uncertain outcomes. The study shows that the number of times a decision is tested (called “trials”) matters in making accurate predictions. The authors examine two types of scenarios: those where rewards are discounted over time and those where average rewards are considered. They provide examples to support their findings, highlighting how the number of trials and the complexity of the problem affect policy evaluation. |
