35 citations · 150 across the 28 of their papers we have counts for
32 papers
Optimal Control of Logically Constrained Partially Observable and Multi-Agent Markov Decision Processes
Krishna C. Kalagarla, Dhruva Kartik, Dongming Shen +3
Autonomous systems often have logical constraints arising, for example, from safety, operational, or regulatory requirements. Such constraints can be expressed using temporal logic…
A Novel Point-based Algorithm for Multi-agent Control Using the Common Information Approach
Dengwang Tang, Ashutosh Nayyar, Rahul Jain
The Common Information (CI) approach provides a systematic way to transform a multi-agent stochastic control problem to a single-agent partially observed Markov decision problem (P…
ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints
Akhil Agnihotri, Rahul Jain, Haipeng Luo
Reinforcement Learning (RL) for constrained MDPs (CMDPs) is an increasingly important problem for various applications. Often, the average criterion is more suitable than the disco…
Optimal Communication and Control Strategies for a Multi-Agent System in the Presence of an Adversary
Dhruva Kartik, Sagar Sudhakara, Rahul Jain +1
We consider a multi-agent system in which a decentralized team of agents controls a stochastic system in the presence of an adversary. Instead of committing to a fixed information…
Learning Infinite-Horizon Average-Reward Markov Decision Processes with Constraints
Liyu Chen, Rahul Jain, Haipeng Luo
We study regret minimization for infinite-horizon average-reward Markov Decision Processes (MDPs) under cost constraints. We start by designing a policy optimization algorithm with…
Implicit Finite-Horizon Approximation and Efficient Optimal Algorithms for Stochastic Shortest Path
Liyu Chen, Mehdi Jafarnia-Jahromi, Rahul Jain +1
We introduce a generic template for developing regret minimization algorithms in the Stochastic Shortest Path (SSP) model, which achieves minimax optimal regret as long as certain…