Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems

Understand

In this work, we study model-based reinforcement learning (RL) in unknown stabilizable linear dynamical systems.

• When learning a dynamical system, one needs to stabilize the unknown dynamics in order to avoid system blow-ups.
• We propose an algorithm that certifies fast stabilization of the underlying system by effectively exploring the environment with an improved exploration strategy.
• We show that the proposed algorithm attains $\tilde{\mathcal{O}}(\sqrt{T})$ regret after $T$ time steps of agent-environment interaction.