REVIEW 2 cited by
Lightning Does Not Strike Twice: Robust MDPs with Coupled Uncertainty
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
We consider Markov decision processes under parameter uncertainty. Previous studies all restrict to the case that uncertainties among different states are uncoupled, which leads to conservative solutions. In contrast, we introduce an intuitive concept, termed "Lightning Does not Strike Twice," to model coupled uncertain parameters. Specifically, we require that the system can deviate from its nominal parameters only a bounded number of times. We give probabilistic guarantees indicating that this model represents real life situations and devise tractable algorithms for computing optimal control policies using this concept.
Forward citations
Cited by 2 Pith papers
-
Dual Formulation for Non-Rectangular Lp Robust Markov Decision Processes
For non-rectangular Lp transition uncertainty, the worst-case return equals the nominal return minus a penalty that can be found by binary search on a fixed-point equation.
-
Robust General Utility for Reinforcement Learning
The paper introduces robust general-utility RL, a minimax formulation over utility uncertainty sets, and proves convergence rates for projected gradient descent-ascent and prox-extragradient algorithms.
Discussion (0). Continue with ORCID to comment.