Thisoptimizationcan beperformedinparallelforeachs, keeping t 1 fixed.

Inthiswork,wefocuson imitation learning, where the aim is to learn how to act in the environment to match the behavior of some unknown targetpolicy [25].

This paper studies a recent proposal to use randomized value functions to drive exploration in reinforcement learning.

However, the combinatorial number of variable subsets prevents direct application of typical RL algorithms.

Reinforcement learning (RL) algorithms often require a large number of data samples to learn a control policy. As a result, training them directly on the real-world systems is expensive and potentially dangerous.