Search Results for author:
Found 23 papers, 2 papers with code
Modeling Human Decision-making in Generalized Gaussian Multi-armed Bandits
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.
Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret Analysis
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.
A Theory of Decision Making Under Dynamic Context
We also show how the model generalizes re- cent work on the control of attention in the Flanker task (Yu et al., 2009).
On Distributed Cooperative Decision-Making in Multiarmed Bandits
We study the explore-exploit tradeoff in distributed cooperative decision-making using the context of the multiarmed bandit (MAB) problem.
Satisficing in multi-armed bandit problems
Satisficing is a relaxation of maximizing and allows for less risky decision making in the face of uncertainty.
Distributed Cooperative Decision-Making in Multiarmed Bandits: Frequentist and Bayesian Algorithms
We study distributed cooperative decision-making under the explore-exploit tradeoff in the multiarmed bandit (MAB) problem.
On Abruptly-Changing and Slowly-Varying Multiarmed Bandit Problems
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#).
On Distributed Multi-player Multiarmed Bandit Problems in Abruptly Changing Environment
We study the multi-player stochastic multiarmed bandit (MAB) problem in an abruptly changing environment.
Distributed Cooperative Decision Making in Multi-agent Multi-armed Bandits
And we consider a constrained reward model in which agents that choose the same arm at the same time receive no reward.
Expedited Multi-Target Search with Guaranteed Performance via Multi-fidelity Gaussian Processes
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.
Minimax Policy for Heavy-tailed Bandits
We study the stochastic Multi-Armed Bandit (MAB) problem under worst-case regret and heavy-tailed reward distribution.
Multi-Robot Gaussian Process Estimation and Coverage: A Deterministic Sequencing Algorithm and Regret Analysis
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.
Nonstationary Stochastic Multiarmed Bandits: UCB Policies and Minimax Regret
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.
In-flight actuator failure recovery of a hexrotor via multiple models and extended high-gain observers
We study an in-flight actuator failure recovery problem for a hexrotor UAV.
A High-Gain Observer Approach to Robust Trajectory Estimation and Tracking for a Multi-rotor UAV
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.
Cloud-Assisted Nonlinear Model Predictive Control for Finite-Duration Tasks
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.
Online Estimation and Coverage Control with Heterogeneous Sensing Information
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.
Towards Modeling Human Motor Learning Dynamics in High-Dimensional Spaces
Designing effective rehabilitation strategies for upper extremities, particularly hands and fingers, warrants the need for a computational model of human motor learning.
Epidemic Propagation under Evolutionary Behavioral Dynamics: Stability and Bifurcation Analysis
We consider the class of SIS epidemic models in which a large population of individuals chooses whether to adopt protection or to remain unprotected as the epidemic evolves.
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning
During exploration, DSEE explores the environment and updates the estimates for expected reward and transition probabilities.
Coupled Evolutionary Behavioral and Disease Dynamics under Reinfection Risk
We study the interplay between epidemic dynamics and human decision making for epidemics that involve reinfection risk; in particular, the susceptible-infected-susceptible (SIS) and the susceptible-infected-recovered-infected (SIRI) epidemic models.
A Multi-Fidelity Bayesian Approach to Safe Controller Design
Safely controlling unknown dynamical systems is one of the biggest challenges in the field of control.
Human Motor Learning Dynamics in High-dimensional Tasks
Conventional approaches to enhancing movement coordination, such as providing instructions and visual feedback, are often inadequate in complex motor tasks with multiple degrees of freedom (DoFs).
