  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, this
article argues 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, again lowering the bar so that more researchers can quickly
investigate different ways that human teachers could assist RL agents,
including learning from demonstrations, learning from feedback, or curriculum
learning.
