让强化学习决策更公平，避免歧视少数群体
PyCFRL: A Python library for counterfactually fair offline reinforcement learning via sequential data preprocessing
- 通过预处理离线数据，实现反事实公平的强化学习
- 可评估策略收益与不公平程度，支持真实场景验证
- 适合关注算法公平性的研究人员和从业者
强化学习旨在学习并评估一种序贯决策规则（即“策略”），以最大化环境中长期的群体利益。然而，尽管强化学习算法优化了整体群体效益，仍可能对少数或经济弱势群体造成不利影响。为此，我们提出 PyCFRL——一个用于确保离线强化学习中反事实公平性的 Python 库。该库实现了一种新颖的数据预处理算法，可在离线数据集上学习反事实公平的强化学习策略，并提供工具评估策略的价值及其反事实不公平水平。本文介绍了 PyCFRL 的核心功能，并通过数据示例展示了其主要应用场景。该库已公开发布于 PyPI 与 GitHub（https://github.com/JianhanZhang/PyCFRL），详细教程可在 PyCFRL 文档（https://pycfrl-documentation.netlify.app）中获取。
原文摘要 · Abstract (English)
Reinforcement learning (RL) aims to learn and evaluate a sequential decision rule, often referred to as a "policy", that maximizes the population-level benefit in an environment across possibly infinitely many time steps. However, the sequential decisions made by an RL algorithm, while optimized to maximize overall population benefits, may disadvantage certain individuals who are in minority or socioeconomically disadvantaged groups. To address this problem, we introduce PyCFRL, a Python library for ensuring counterfactual fairness in offline RL. PyCFRL implements a novel data preprocessing algorithm for learning counterfactually fair RL policies from offline datasets and provides tools to evaluate the values and counterfactual unfairness levels of RL policies. We describe the high-level functionalities of PyCFRL and demonstrate one of its major use cases through a data example. The library is publicly available on PyPI and Github (https://github.com/JianhanZhang/PyCFRL), and detailed tutorials can be found in the PyCFRL documentation (https://pycfrl-documentation.netlify.app).
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