Search Results for author: Vaibhav Srivastava

Found 17 papers, 2 papers with code

Online Estimation and Coverage Control with Heterogeneous Sensing Information

1 code implementation28 Jun 2021 Andrew McDonald, Lai Wei, Vaibhav Srivastava

In this paper, we address the problem of multi-robot online estimation and coverage control by combining low- and high-fidelity data to learn and cover a sensory function of interest.

Cloud-Assisted Nonlinear Model Predictive Control for Finite-Duration Tasks

no code implementations20 Jun 2021 Nan Li, Kaixiang Zhang, Zhaojian Li, Vaibhav Srivastava, Xiang Yin

In this paper, we propose a novel cloud-assisted model predictive control (MPC) framework in which we systematically fuse a cloud MPC that uses a high-fidelity nonlinear model but is subject to communication delays with a local MPC that exploits simplified dynamics (due to limited computation) but has timely feedback.

A High-Gain Observer Approach to Robust Trajectory Estimation and Tracking for a Multi-rotor UAV

no code implementations24 Mar 2021 Connor J Boss, Vaibhav Srivastava

We study the problem of estimating and tracking an unknown trajectory with a multi-rotor UAV in the presence of modeling error and external disturbances.

Nonstationary Stochastic Multiarmed Bandits: UCB Policies and Minimax Regret

no code implementations22 Jan 2021 Lai Wei, Vaibhav Srivastava

We study the nonstationary stochastic Multi-Armed Bandit (MAB) problem in which the distribution of rewards associated with each arm are assumed to be time-varying and the total variation in the expected rewards is subject to a variation budget.

Multi-Robot Gaussian Process Estimation and Coverage: A Deterministic Sequencing Algorithm and Regret Analysis

no code implementations12 Jan 2021 Lai Wei, Andrew McDonald, Vaibhav Srivastava

Modeling the sensory field as a realization of a Gaussian Process and using Bayesian techniques, we devise a policy which aims to balance the tradeoff between learning the sensory function and covering the environment.

Minimax Policy for Heavy-tailed Bandits

no code implementations20 Jul 2020 Lai Wei, Vaibhav Srivastava

We study the stochastic Multi-Armed Bandit (MAB) problem under worst-case regret and heavy-tailed reward distribution.

Multi-Armed Bandits

Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes

no code implementations18 May 2020 Lai Wei, Xiaobo Tan, Vaibhav Srivastava

Based on the sensing model, we design a novel algorithm called Expedited Multi-Target Search (EMTS) that (i) addresses the coverage-accuracy trade-off: sampling at locations farther from the floor provides wider field of view but less accurate measurements, (ii) computes an occupancy map of the floor within a prescribed accuracy and quickly eliminates unoccupied regions from the search space, and (iii) travels efficiently to collect the required samples for target detection.

Gaussian Processes

Distributed Cooperative Decision Making in Multi-agent Multi-armed Bandits

no code implementations3 Mar 2020 Peter Landgren, Vaibhav Srivastava, Naomi Ehrich Leonard

And we consider a constrained reward model in which agents that choose the same arm at the same time receive no reward.

Decision Making Multi-Armed Bandits

On Distributed Multi-player Multiarmed Bandit Problems in Abruptly Changing Environment

no code implementations12 Dec 2018 Lai Wei, Vaibhav Srivastava

We study the multi-player stochastic multiarmed bandit (MAB) problem in an abruptly changing environment.

On Abruptly-Changing and Slowly-Varying Multiarmed Bandit Problems

no code implementations23 Feb 2018 Lai Wei, Vaibhav Srivastava

We study the non-stationary stochastic multiarmed bandit (MAB) problem and propose two generic algorithms, namely, the limited memory deterministic sequencing of exploration and exploitation (LM-DSEE) and the Sliding-Window Upper Confidence Bound# (SW-UCB#).

Distributed Cooperative Decision-Making in Multiarmed Bandits: Frequentist and Bayesian Algorithms

no code implementations2 Jun 2016 Peter Landgren, Vaibhav Srivastava, Naomi Ehrich Leonard

We study distributed cooperative decision-making under the explore-exploit tradeoff in the multiarmed bandit (MAB) problem.

Decision Making

Satisficing in multi-armed bandit problems

no code implementations23 Dec 2015 Paul Reverdy, Vaibhav Srivastava, Naomi Ehrich Leonard

Satisficing is a relaxation of maximizing and allows for less risky decision making in the face of uncertainty.

Decision Making

On Distributed Cooperative Decision-Making in Multiarmed Bandits

no code implementations21 Dec 2015 Peter Landgren, Vaibhav Srivastava, Naomi Ehrich Leonard

We study the explore-exploit tradeoff in distributed cooperative decision-making using the context of the multiarmed bandit (MAB) problem.

Decision Making

A Theory of Decision Making Under Dynamic Context

1 code implementation NeurIPS 2015 Michael Shvartsman, Vaibhav Srivastava, Jonathan D. Cohen

We also show how the model generalizes re- cent work on the control of attention in the Flanker task (Yu et al., 2009).

Decision Making

Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret Analysis

no code implementations5 Jul 2015 Vaibhav Srivastava, Paul Reverdy, Naomi Ehrich Leonard

We consider the correlated multiarmed bandit (MAB) problem in which the rewards associated with each arm are modeled by a multivariate Gaussian random variable, and we investigate the influence of the assumptions in the Bayesian prior on the performance of the upper credible limit (UCL) algorithm and a new correlated UCL algorithm.

Decision Making

Modeling Human Decision-making in Generalized Gaussian Multi-armed Bandits

no code implementations23 Jul 2013 Paul Reverdy, Vaibhav Srivastava, Naomi E. Leonard

We develop the upper credible limit (UCL) algorithm for the standard multi-armed bandit problem and show that this deterministic algorithm achieves logarithmic cumulative expected regret, which is optimal performance for uninformative priors.

Bayesian Inference Decision Making +1

Cannot find the paper you are looking for? You can Submit a new open access paper.