Derives |Z|-free minimax PAC bounds for policy evaluation and best-policy extraction in exogenous contextual tabular MDPs under oracle access regimes.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
This review synthesizes existing RL-MPC integration methods for linear systems into a taxonomy across RL roles, algorithms, MPC formulations, costs, and domains while identifying recurring patterns and practical challenges.
citing papers explorer
-
Minimax PAC Bounds for Learning in Exogenous Contextual MDPs
Derives |Z|-free minimax PAC bounds for policy evaluation and best-policy extraction in exogenous contextual tabular MDPs under oracle access regimes.
-
A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems
This review synthesizes existing RL-MPC integration methods for linear systems into a taxonomy across RL roles, algorithms, MPC formulations, costs, and domains while identifying recurring patterns and practical challenges.