Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information
存在私人信息的人类引导人机交互中的离线强化学习AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确以工具变量识别处理私人信息造成的未测量混杂，并据此构造策略评价方法。
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存在私人信息的人类引导人机交互中的离线强化学习
- 研究问题
- 在人与机器轮流行动、机器私人信息不可观测的交互中，如何利用既有离线数据评价并学习双方策略，同时应对混杂与分布不匹配？
- 方法与思路
- 作者把交互建模为双人轮流博弈，以机器先前行动作为人类当前决策的工具变量，建立离策略评价方法，再结合悲观原则学习策略对。
- 材料陈述的贡献
- 摘要称，作者给出新的识别结果，并证明在离线数据部分覆盖等条件下，学习得到的策略对以一定速率收敛到最优策略对。
- 阅读关注点
- 应核对先前行动满足工具变量条件的理由、私人信息的时间结构、部分覆盖的精确定义，以及评价误差如何传递到策略学习。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未获核验；摘要未列出工具变量假设、覆盖条件和收敛速率，不能推断任意离线数据均可学习。
原文摘要
Motivated by the human-machine interaction such as training chatbots for improving customer satisfaction, we study human-guided human-machine interaction involving private information. We model this interaction as a two-player turn-based game, where one player (Alice, a human) guides the other player (Bob, a machine) towards a common goal. Specifically, we focus on offline reinforcement learning (RL) in this game, where the goal is to find a policy pair for Alice and Bob that maximizes their expected total rewards based on an offline dataset collected a priori. The offline setting presents two challenges: (i) We cannot collect Bob's private information, leading to a confounding bias when using standard RL methods, and (ii) a distributional mismatch between the behavior policy used to collect data and the desired policy we aim to learn. To tackle the confounding bias, we treat Bob's previous action as an instrumental variable for Alice's current decision making so as to adjust for the unmeasured confounding. We develop a novel identification result and use it to propose a new off-policy evaluation (OPE) method for evaluating policy pairs in this two-player turn-based game. To tackle the distributional mismatch, we leverage the idea of pessimism and use our OPE method to develop an off-policy learning algorithm for finding a desirable policy pair for both Alice and Bob. Finally, we prove that under mild assumptions such as partial coverage of the offline data, the policy pair obtained through our method converges to the optimal one at a satisfactory rate.