Optimistic Sampling Strategy for Data-Efficient Reinforcement Learning
A high required number of interactions with the environment is one of the most important problems in reinforcement learning (RL).To deal with this problem, several data-efficient RL algorithms have been proposed and successfully applied in practice.Unlike previous research, that focuses on giantmouse jagt optimal policy evaluation and policy improv