论文标题
爬行动物：一个积极主动的实时强化学习自适应框架
REPTILE: A Proactive Real-Time Deep Reinforcement Learning Self-adaptive Framework
论文作者
论文摘要
在这项工作中，提出了一个一般框架，以支持能够根据操作环境变化调整其行为的软件系统的开发。所提出的方法名为Reptile，以一种完整的主动方式起作用，并依赖于基于强化学习的代理人对事件的反应（称为新颖性），可能会影响系统的预期行为。在我们的框架中，考虑了两种类型的新颖性：与上下文/环境有关的新颖性以及与物理体系结构本身相关的新颖性。该框架在发生之前预测这些新颖性，提取了环境改变时间的模型，并使用合适的马尔可夫决策过程来处理实时设置。此外，我们的RL代理的体系结构根据可以采取的可能行动而发展。
In this work a general framework is proposed to support the development of software systems that are able to adapt their behaviour according to the operating environment changes. The proposed approach, named REPTILE, works in a complete proactive manner and relies on Deep Reinforcement Learning-based agents to react to events, referred as novelties, that can affect the expected behaviour of the system. In our framework, two types of novelties are taken into account: those related to the context/environment and those related to the physical architecture itself. The framework, predicting those novelties before their occurrence, extracts time-changing models of the environment and uses a suitable Markov Decision Process to deal with the real-time setting. Moreover, the architecture of our RL agent evolves based on the possible actions that can be taken.
