Knowledge reuse for deep reinforcement learning. - Glatt, Ruben (2019)
With the rise of Deep Learning the field of Artificial Intelligence (AI) Research has entered a new era. Together with an increasing amount of data and vastly improved computing capabilities, Machine Learning builds the backbone of AI, providing many of the tools and algorithms that drive development and applications. While we have already achieved many successes in the fields of image recognition, language processing, recommendation engines, robotics, or autonomous systems, most progress was achieved when the algorithms were focused on learning only a single task with little regard to effort and reusability. Since learning a new task from scratch often involves an expensive learning process, in this work, we are considering the use of previously acquired knowledge to speed up the learning of a new task. For that, we investigated the application of Transfer Learning methods for Deep Reinforcement Learning (DRL) agents and propose a novel framework for knowledge preservation and reuse. We show, that the knowledge transfer can make a big difference if the source knowledge is chosen carefully in a systematic approach. To get to this point, we provide an overview of existing literature of methods that realize knowledge transfer for DRL, a field which has been starting to appear frequently in the relevant literature only in the last two years. We then formulate the Case-based Reasoning methodology, which describes a framework for knowledge reuse in general terms, in Reinforcement Learning terminology to facilitate the adaption and communication between the respective communities. Building on this framework, we propose Deep Case-based Policy Inference (DECAF) and demonstrate in an experimental evaluation the usefulness of our approach for sequential task learning with knowledge preservation and reuse. Our results highlight the benefits of knowledge transfer while also making aware of the challenges that come with it. We consider the work in this area as an important step towards more stable general learning agents that are capable of dealing with the most complex tasks, which would be a key achievement towards Artificial General Intelligence.