论文标题
方案辅助深入的强化学习
Scenario-Assisted Deep Reinforcement Learning
论文作者
论文摘要
事实证明，深厚的强化学习在非结构化数据的培训代理中非常有用。但是，生产的代理的不透明度使得难以确保它们遵守人类工程师提出的各种要求。在此过程中的报告中，我们提出了一种技术，以增强强化学习培训过程（特别是其奖励计算）的技术，以使人类工程师可以直接贡献其专家知识，从而使培训中的代理商更有可能遵守各种相关约束。此外，我们提出的方法允许使用高级模型工程技术（例如基于方案的建模）来制定这些约束。这种基于黑箱学习的工具与经典建模方法的混合在一起可以产生有效和有效的系统，但也更加透明和可维护。我们使用互联网拥塞控制领域的案例研究来评估我们的技术，从而获得了有希望的结果。
Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements posed by human engineers. In this work-in-progress report, we propose a technique for enhancing the reinforcement learning training process (specifically, its reward calculation), in a way that allows human engineers to directly contribute their expert knowledge, making the agent under training more likely to comply with various relevant constraints. Moreover, our proposed approach allows formulating these constraints using advanced model engineering techniques, such as scenario-based modeling. This mix of black-box learning-based tools with classical modeling approaches could produce systems that are effective and efficient, but are also more transparent and maintainable. We evaluated our technique using a case-study from the domain of internet congestion control, obtaining promising results.
