Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym
This work aims to lower the barrier for researchers investigating human assistance in reinforcement learning by providing a new open-source platform.
Reinforcement learning (RL) agents often suffer from long learning times and high data requirements due to initial random actions. This paper proposes that an external human teacher can significantly accelerate RL agent learning and introduces HIPPO Gym, an open-source framework designed to facilitate human-RL research.
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.