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Reinforcement learning (RL) is a popular machine learning paradigm for gameplaying, robotics control, and other sequential decision tasks. However, RLagents often have long learning times with high data requirements because theybegin by acting randomly. In order to better learn in complex tasks, thisarticle argues that an external teacher can often significantly help the RLagent learn.  OpenAI Gym is a common framework for RL research, including a large number ofstandard environments and agents, making RL research significantly moreaccessible. This article introduces our new open-source RL framework, the HumanInput Parsing Platform for Openai Gym (HIPPO Gym), and the design decisionsthat went into its creation. The goal of this platform is to facilitatehuman-RL research, again lowering the bar so that more researchers can quicklyinvestigate different ways that human teachers could assist RL agents,including learning from demonstrations, learning from feedback, or curriculumlearning.

Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym