SHARPIE是支持人机强化学习实验的模块化平台，便于研究交互机制。
SHARPIE: A Modular Framework for Reinforcement Learning and Human-AI Interaction Experiments
- 模块化设计，集成环境、算法与人机交互界面
- 支持多种人机交互场景，如反馈学习与任务协同
- 适用于研究人机协作、偏好获取等方向的学者
强化学习（RL）为建模和训练智能体，包括人机交互场景，提供了一种通用方法。本文提出SHARPIE（面向交互实验的共享人机强化学习平台），以满足支持人机强化学习实验的通用框架需求。其模块化设计包含灵活的RL环境与算法封装、面向参与者的网页界面、日志记录工具，以及在主流云平台和参与者招募平台上的部署能力。该平台使研究人员能够探索广泛的研究问题，包括交互式奖励设定与学习、从人类反馈中学习、动作委托、偏好获取、用户建模及人机协同。平台基于通用的人机-强化学习交互接口，旨在标准化人机情境下强化学习的研究领域。
原文摘要 · Abstract (English)
Reinforcement learning (RL) offers a general approach for modeling and training AI agents, including human-AI interaction scenarios. In this paper, we propose SHARPIE (Shared Human-AI Reinforcement Learning Platform for Interactive Experiments) to address the need for a generic framework to support experiments with RL agents and humans. Its modular design consists of a versatile wrapper for RL environments and algorithm libraries, a participant-facing web interface, logging utilities, deployment on popular cloud and participant recruitment platforms. It empowers researchers to study a wide variety of research questions related to the interaction between humans and RL agents, including those related to interactive reward specification and learning, learning from human feedback, action delegation, preference elicitation, user-modeling, and human-AI teaming. The platform is based on a generic interface for human-RL interactions that aims to standardize the field of study on RL in human contexts.
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