10 citations · 16 across the 18 of their papers we have counts for
5 papers · 1 filter
Certifying Almost-Sure Reachability in Unknown MDPs
S Akshay, Suguman Bansal, Gaurav Mahla
{\em Specification-Guided Reinforcement Learning (RL)} has largely concerned itself with \emph{learning} an optimal policy against a formal specification in an unknown {\em Markov…
Q-Learning for Reachability in MEC-Free MDPs
Lu-Chin Chang, Suguman Bansal
Reinforcement learning (RL) for reachability specifications is fundamental to sequential decision-making. Prior work establishes asymptotic convergence to optimal policies, but onl…
Certificate-Guided Evaluation of Reinforcement Learning Generalization
Vignesh Subramanian, Đorđe Žikelić, Suguman Bansal
This work presents a logic-driven framework to evaluate the performance of reinforcement learning (RL) algorithms in their ability to generalize to unseen tasks. Our framework defi…
Decoupled Behavioral Cloning for Scalable Inductive Generalization in RL from Specifications
Vignesh Subramanian, Subhajit Roy, Suguman Bansal
Inductive generalization is a framework for reinforcement learning (RL) generalization in which inductively related task instances admit inductively related policies. Prior work ca…
Reinforcement Learning for Reachability: Guaranteeing Asymptotic Optimality
Amogh Palasamudram, Jakub Svoboda, Suguman Bansal +1
Reinforcement learning (RL) for reachability specifications is fundamental in sequential decision-making, yet theoretical guarantees remain less explored. A recent work achieves as…