论文标题
通过明确表示代理行为的概括
Generalization of Agent Behavior through Explicit Representation of Context
论文作者
论文摘要
为了在数字互动环境中部署自主代理，它们必须能够在看不见的情况下采取强烈的行动。标准的机器学习方法是将尽可能多的变化包括在培训这些代理中。然后，代理商可以在训练中插入，但他们不能推断出超越它的训练。本文提出了一种有原则的方法，其中上下文模块与游戏中的技能模块共同发展。上下文模块识别游戏的时间变化，并调节技能模块的输出，以便即使在以前看不见的情况下也可以做出稳健的行动决策。该方法在Flappy Bird和Lunarlander视频游戏以及Carla自动驾驶模拟中进行了评估。上下文+技能方法会导致在需要推断训练之外推断的环境中明显更强的行为。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适应的基础。
In order to deploy autonomous agents in digital interactive environments, they must be able to act robustly in unseen situations. The standard machine learning approach is to include as much variation as possible into training these agents. The agents can then interpolate within their training, but they cannot extrapolate much beyond it. This paper proposes a principled approach where a context module is coevolved with a skill module in the game. The context module recognizes the temporal variation in the game and modulates the outputs of the skill module so that the action decisions can be made robustly even in previously unseen situations. The approach is evaluated in the Flappy Bird and LunarLander video games, as well as in the CARLA autonomous driving simulation. The Context+Skill approach leads to significantly more robust behavior in environments that require extrapolation beyond training. Such a principled generalization ability is essential in deploying autonomous agents in real-world tasks, and can serve as a foundation for continual adaptation as well.
