在人类协助下改进强化学习：与HIPPO Gym一起进行人类主题研究的论点
Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym
摘要：
强化学习（RL）是一种流行的机器学习范例，用于游戏，机器人控制和其他顺序决策任务。然而，RL代理通常具有很长的学习时间和很高的数据要求，因为它们开始时是随机的。为了在复杂任务中更好地学习，本文认为外部教师通常可以显着帮助RL Agent学习。OpenAI Gym是RL研究的通用框架，包括大量的标准环境和代理，使RL研究变得更加容易。本文介绍了我们新的开源RL框架，Openai Gym的人类输入解析平台（HIPPO Gym），以及创建它的设计决策。这个平台的目标是促进人类RL研究，再次降低门槛，以便更多的研究人员可以快速研究人类教师可以帮助RL代理的不同方式，包括从演示中学习示教学习，从反馈中学习或课程学习。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
