10 citations · 14 across the 6 of their papers we have counts for
6 papers
Policy Synthesis and Reinforcement Learning for Discounted LTL
Rajeev Alur, Osbert Bastani, Kishor Jothimurugan +3
The difficulty of manually specifying reward functions has led to an interest in using linear temporal logic (LTL) to express objectives for reinforcement learning (RL). However, L…
Robust Subtask Learning for Compositional Generalization
Kishor Jothimurugan, Steve Hsu, Osbert Bastani +1
Compositional reinforcement learning is a promising approach for training policies to perform complex long-horizon tasks. Typically, a high-level task is decomposed into a sequence…
Specification-Guided Learning of Nash Equilibria with High Social Welfare
Kishor Jothimurugan, Suguman Bansal, Osbert Bastani +1
Reinforcement learning has been shown to be an effective strategy for automatically training policies for challenging control problems. Focusing on non-cooperative multi-agent syst…
A Framework for Transforming Specifications in Reinforcement Learning
Rajeev Alur, Suguman Bansal, Osbert Bastani +1
Reactive synthesis algorithms allow automatic construction of policies to control an environment modeled as a Markov Decision Process (MDP) that are optimal with respect to high-le…
Compositional Reinforcement Learning from Logical Specifications
Kishor Jothimurugan, Suguman Bansal, Osbert Bastani +1
We study the problem of learning control policies for complex tasks given by logical specifications. Recent approaches automatically generate a reward function from a given specifi…
Learning Algorithms for Regenerative Stopping Problems with Applications to Shipping Consolidation in Logistics
Kishor Jothimurugan, Matthew Andrews, Jeongran Lee +1
We study regenerative stopping problems in which the system starts anew whenever the controller decides to stop and the long-term average cost is to be minimized. Traditional model…