Improving reinforcement learning with human assistance: an argument for human subject studies with HIPPO Gym

Reinforcement learning (RL) is a popular machine learning paradigm for game playing, robotics control, and other sequential decision tasks. However, RL agents often have long learning times with high data requirements because they begin by acting randomly. In order to better learn in complex tasks, we argue that an external teacher can often significantly help the RL agent learn. OpenAI Gym is a common framework for RL research, including a large number of standard environments and agents, making RL research significantly more accessible. This article introduces our new open-source RL framework, the Human Input Parsing Platform for Openai Gym (HIPPO Gym), and the design decisions that went into its creation. The goal of this platform is to facilitate human-RL research, making human-in-the-loop RL more accessible, including learning from demonstrations, learning from feedback, or curriculum learning. In addition, all experiments can be conducted over the internet without any additional software needed on the client's computer, making experiments at scale significantly easier.