这个论文搞了个新训练方式，让预测器和充电策略一起优化，结果充电失败少了一半多，值得做RL调度的看看。
一篇论文提出决策聚焦强化学习（DF-RL）框架，用于控制电动汽车充电，解决未知离开时间问题。该方法将预测器与充电策略进行端到端联合训练，相比不使用离开时间预测的RL方法，总奖励提升14%，未供应能量（因车辆提前离开导致充电失败）减少55%。实验基于历史数据模拟，验证了在不确定性下充电决策质量的改善。
Forecasting what Matters: Decision-Focused RL for Controlled EV Charging with Unknown Departure Times
The recent growth of EV adoption poses challenges for power systems, including increased peak demand and potential grid instability. Smart control of EV charging -- e.g., based on reinforcement learning (RL) -- can alleviate these issues by learning temporal and contextual patterns from historical data. Yet, in real-world scenarios, key features, such as departure time, often are unavailable. This, in turn, makes it harder for an RL agent to learn and execute an effective charging policy. To mitigate this uncertainty, a trained forecaster can approximate the unknown features from available data. However, since these forecasting models are typically trained for accuracy (rather than their impact on a downstream agent's decision quality), their errors may propagate and hinder the overall performance of a controller that is using the forecasts. To avoid this, we propose a decision-focused RL (DF-RL) framework in which the forecaster is trained end-to-end, i.e., with feedback from the charging policy actions taken by the RL agent. Such joint training of both the forecaster and controller ultimately results in higher-quality actions: our proposed DF-RL method yields superior charging decisions compared to other baselines, achieving up to a 14% improvement in total reward and a 55% reduction of unsupplied energy (i.e., charging that failed to happen because the EV already left), relative to the RL method without departure time forecasting.