Summary of Certifiably Robust Policies For Uncertain Parametric Environments, by Yannik Schnitzer et al.
Certifiably Robust Policies for Uncertain Parametric Environments
by Yannik Schnitzer, Alessandro Abate, David Parker
First submitted to arxiv on: 6 Aug 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 This paper proposes a data-driven method for generating policies that are provably robust across unknown stochastic environments. The existing approaches can learn models of a single environment as an interval Markov decision process (IMDP) and produce a robust policy with a probably approximately correct (PAC) guarantee on its performance. However, these approaches are unable to reason about the impact of environmental parameters underlying the uncertainty. The proposed framework is based on parametric Markov decision processes (MDPs) with unknown distributions over parameters. The key challenge is then to produce meaningful performance guarantees that combine the two layers of uncertainty: (1) multiple environments induced by parameters with an unknown distribution; (2) unknown induced environments which are approximated by IMDPs. The paper presents a novel approach based on scenario optimisation that yields a single PAC guarantee quantifying the risk level for which a specified performance level can be assured in unseen environments, plus a means to trade-off risk and performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better decisions in situations where things might not go exactly as planned. It creates a system that can learn from different scenarios and predict how well it will do in new ones. The system uses something called Markov decision processes, which are like maps of possible outcomes. The goal is to create a plan that works well no matter what happens. The paper shows how this system can be used to make good decisions by combining all the different possibilities into one overall plan. |
