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Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation
To the best of our knowledge, our algorithm is the first efficient rate optimal regret minimization algorithm for adversarial CMDPs that operates under the minimal standard assumption of online function approximation.
Eluder-based Regret for Stochastic Contextual MDPs
To the best of our knowledge, our algorithm is the first efficient and rate-optimal regret minimization algorithm for CMDPs that operates under the general offline function approximation setting.
Rate-Optimal Online Convex Optimization in Adaptive Linear Control
We consider the problem of controlling an unknown linear dynamical system under adversarially changing convex costs and full feedback of both the state and cost function.
Efficient Online Linear Control with Stochastic Convex Costs and Unknown Dynamics
We consider the problem of controlling an unknown linear dynamical system under a stochastic convex cost and full feedback of both the state and cost function.
Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with $\sqrt{T}$ Regret
We consider the task of learning to control a linear dynamical system under fixed quadratic costs, known as the Linear Quadratic Regulator (LQR) problem.
Bandit Linear Control
We consider the problem of controlling a known linear dynamical system under stochastic noise, adversarially chosen costs, and bandit feedback.
Logarithmic Regret for Learning Linear Quadratic Regulators Efficiently
We consider the problem of learning in Linear Quadratic Control systems whose transition parameters are initially unknown.
A General Framework for Bandit Problems Beyond Cumulative Objectives
Unlike the case of cumulative criteria, in the problems we study here the oracle policy, that knows the problem parameters a priori and is used to "center" the regret, is not trivial.
