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Poster
Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with √T Regret
Asaf Cassel · Tomer Koren

Wed Jul 21 09:00 AM -- 11:00 AM (PDT) @
in Poster Session 3 »

We consider the task of learning to control a linear dynamical system under fixed quadratic costs, known as the Linear Quadratic Regulator (LQR) problem. While model-free approaches are often favorable in practice, thus far only model-based methods, which rely on costly system identification, have been shown to achieve regret that scales with the optimal dependence on the time horizon T. We present the first model-free algorithm that achieves similar regret guarantees. Our method relies on an efficient policy gradient scheme, and a novel and tighter analysis of the cost of exploration in policy space in this setting.

Keywords: RL, Decisions and Control Theory

Author Information

Asaf Cassel (Tel Aviv University)
Tomer Koren (Tel Aviv University and Google)

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