12 citations · 17 across the 7 of their papers we have counts for
7 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…
Safe Posterior Sampling for Constrained MDPs with Bounded Constraint Violation
Krishna C Kalagarla, Rahul Jain, Pierluigi Nuzzo
Constrained Markov decision processes (CMDPs) model scenarios of sequential decision making with multiple objectives that are increasingly important in many applications. However,…
Optimal Control of Partially Observable Markov Decision Processes with Finite Linear Temporal Logic Constraints
Krishna C. Kalagarla, Dhruva Kartik, Dongming Shen +3
Autonomous agents often operate in scenarios where the state is partially observed. In addition to maximizing their cumulative reward, agents must execute complex tasks with rich t…
Model-Free Reinforcement Learning for Optimal Control of MarkovDecision Processes Under Signal Temporal Logic Specifications
Krishna C. Kalagarla, Rahul Jain, Pierluigi Nuzzo
We present a model-free reinforcement learning algorithm to find an optimal policy for a finite-horizon Markov decision process while guaranteeing a desired lower bound on the prob…
Synthesis of Discounted-Reward Optimal Policies for Markov Decision Processes Under Linear Temporal Logic Specifications
Krishna C. Kalagarla, Rahul Jain, Pierluigi Nuzzo
We present a method to find an optimal policy with respect to a reward function for a discounted Markov decision process under general linear temporal logic (LTL) specifications. P…
Designing Interpretable Approximations to Deep Reinforcement Learning
Nathan Dahlin, Krishna Chaitanya Kalagarla, Nikhil Naik +2
In an ever expanding set of research and application areas, deep neural networks (DNNs) set the bar for algorithm performance. However, depending upon additional constraints such a…
A Sample-Efficient Algorithm for Episodic Finite-Horizon MDP with Constraints
Krishna C. Kalagarla, Rahul Jain, Pierluigi Nuzzo
Constrained Markov Decision Processes (CMDPs) formalize sequential decision-making problems whose objective is to minimize a cost function while satisfying constraints on various c…