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20192025
most citedA Sample-Efficient Algorithm for Episodic Finite-Horizon MDP with Constraints

12 citations · 18 across the 10 of their papers we have counts for

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5 papers · 1 filter

eess.SY20221 cited

Control Barrier Function Contracts for Vehicular Mission Planning Under Signal Temporal Logic Specifications

Muhammad Waqas, Nikhil Vijay Naik, Petros Ioannou +1

We present a compositional control synthesis method based on assume-guarantee contracts with application to correct-by-construction design of vehicular mission plans. In our approa…

eess.SY2022

Correct-By-Construction Design of Adaptive Cruise Control with Control Barrier Functions Under Safety and Regulatory Constraints

Muhammad Waqas, Muhammad Ali Murtaza, Pierluigi Nuzzo +1

The safety-critical nature of adaptive cruise control (ACC) systems calls for systematic design procedures, e.g., based on formal methods or control barrier functions (CBFs), to pr…

eess.SY2022

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…

eess.SY2021

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…

eess.SY20204 cited

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…