Blessing from Human-AI Interaction: Super Reinforcement Learning in Confounded Environments
人机交互的助益：混杂环境中的超级强化学习AI 解读 ↓
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人机交互的助益：混杂环境中的超级强化学习
- 研究问题
- 在存在未测量混杂的序贯决策中，能否利用人类或人工智能已经采取的行动所包含的信息，学习表现更好的决策策略？
- 方法与思路
- 作者将已观察行动纳入策略输入，并在近端因果推断框架下建立非参数识别结果，据此开发批量数据上的超级策略学习算法。
- 材料陈述的贡献
- 摘要称，超级策略在其理论框架内保证优于标准最优策略与行为策略，并给出识别结果、有限样本遗憾保证及模拟和应用展示。
- 阅读关注点
- 应核对既有行动何时可作为决策输入、近端识别所需条件，以及超级策略与标准策略是否使用不同信息集和相同评价标准。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收均未核实；摘要未说明代理变量、识别条件及优势保证的具体适用范围。
原文摘要
As AI becomes more prevalent throughout society, effective methods of integrating humans and AI systems that leverage their respective strengths and mitigate risk have become an important priority. In this paper, we introduce the paradigm of super policy learning that takes advantage of Human-AI interaction for data driven sequential decision making. This approach utilizes the observed action, either from AI or humans, as input for achieving a stronger oracle in policy learning for the decision maker (humans or AI). In the decision process with unmeasured confounding, the actions taken by past agents can offer valuable insights into undisclosed information. By including this information for the policy search in a novel and legitimate manner, the proposed super policy learning will yield a super-policy that is guaranteed to outperform both the standard optimal policy and the behavior one (e.g., past agents' actions). We call this stronger oracle a blessing from human-AI interaction. Furthermore, to address the issue of unmeasured confounding in finding super-policies using the batch data, a number of nonparametric and causal identifications are established under the framework of proximal causal inference. Building upon on these novel identification results, we develop several super-policy learning algorithms and systematically study their theoretical properties such as finite-sample regret guarantee. Finally, we illustrate the effectiveness of our proposal through extensive simulations and real-world applications.