This work serves as a review of our experience applying off-policy techniques to train and evaluate a contextual bandit model powering a troubleshooting notification in a chatbot. First, we demonstrate the effectiveness of off-policy evaluation when data volume is orders of magnitude less than typically found in the literature. We present our reward function and choices behind its design, as well as how we construct our logging policy to balance exploration and performance on key metrics. Next, we present a guided framework to update a model post-training called Post-Hoc Reward Distribution Hacking, which we employed to improve model performance and correct deficiencies in trained models stemming from the existence of a null action and a noisy reward signal. Throughout the work, we include discussions of various practical pitfalls encountered while using off-policy methods in hopes to expedite other applications of these techniques.

这项工作是对我们应用非策略技术来训练和评估上下文强盗模型的经验的回顾，该模型为聊天机器人中的故障排除通知提供了动力。首先，我们证明了当数据量比文献中通常所见数量少几个数量级时，非政策评估的有效性。我们介绍了奖励功能及其设计背后的选择，以及我们如何构造日志记录策略以平衡关键指标的探索和性能。接下来，我们提出了一个指导框架，用于更新称为“事后奖励分布黑客”的模型后训练，该模型用于改善模型性能并纠正由于存在无效动作和嘈杂奖励信号而导致的训练后模型中的缺陷。在整个工作中