Value Driven Representation for Human-in-the-Loop Reinforcement Learning
  Interactive adaptive systems powered by Reinforcement Learning (RL) have many
potential applications, such as intelligent tutoring systems. In such systems
there is typically an external human system designer that is creating,
monitoring and modifying the interactive adaptive system, trying to improve its
performance on the target outcomes. In this paper we focus on algorithmic
foundation of how to help the system designer choose the set of sensors or
features to define the observation space used by reinforcement learning agent.
We present an algorithm, value driven representation (VDR), that can
iteratively and adaptively augment the observation space of a reinforcement
learning agent so that is sufficient to capture a (near) optimal policy. To do
so we introduce a new method to optimistically estimate the value of a policy
using offline simulated Monte Carlo rollouts. We evaluate the performance of
our approach on standard RL benchmarks with simulated humans and demonstrate
significant improvement over prior baselines.
