A Markovian Formalism for Active Querying

Current reinforcement learning policies are heavily dependent upon the usage of a reward function. However, for advanced problems such as learning behavior for a self-driving car or a robot with many degrees of freedom, the reward function is very difficult or impossible to design to effectively encompass all human considerations. Inverse reinforcement learning algorithms are a class of algorithms that attempt to solve this issue by learning a reward function from expert demonstrations, and then subsequently using any manner of standard reinforcement learning algorithms to learn an optimal policy based off that learned reward function [1]. Inverse reinforcement learning algorithms require a dataset of expert demonstrations to derive a reward function from. However, this reward function can often be inaccurate and highly variable.