论文标题
通过明确表示上下文来适应看不见的环境
Adapting to Unseen Environments through Explicit Representation of Context
论文作者
论文摘要
为了将自主代理部署到诸如自动驾驶，基础设施管理，医疗保健和金融等领域，他们必须能够安全地适应看不见的情况。当前构建此类药物的方法是尝试将尽可能多的变化包括在训练中，然后在可能的变化中概括。本文提出了一种有原则的方法，其中上下文模块与技能模块共同发展。上下文模块识别变化并调节技能模块，以使整个系统在看不见的情况下表现良好。该方法是在充满挑战的鸟类游戏中评估的，随着时间的流逝，动作的影响会有所不同。上下文+技能方法会导致在以前看不见的效果的环境中明显更强的鲁棒行为。这种原则性的概括能力对于在现实世界任务中部署自主代理至关重要，并且也可以作为持续学习的基础。
In order to deploy autonomous agents to domains such as autonomous driving, infrastructure management, health care, and finance, they must be able to adapt safely to unseen situations. The current approach in constructing such agents is to try to include as much variation into training as possible, and then generalize within the possible variations. This paper proposes a principled approach where a context module is coevolved with a skill module. The context module recognizes the variation and modulates the skill module so that the entire system performs well in unseen situations. The approach is evaluated in a challenging version of the Flappy Bird game where the effects of the actions vary over time. The Context+Skill approach leads to significantly more robust behavior in environments with previously unseen effects. Such a principled generalization ability is essential in deploying autonomous agents in real world tasks, and can serve as a foundation for continual learning as well.
