Advances and challenges in learning from experience replay

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From the first theoretical propositions in the 1950s to its application in real-world problems, Reinforcement Learning (RL) is still a fascinating and complex class of machine learning algorithms with overgrowing literature in recent years. In this work, we present an extensive and structured literature review and discuss how the Experience Replay (ER) technique has been fundamental in making various RL methods in most relevant problems and different domains more data efficient. ER is the central focus of this review. One of its main contributions is a taxonomy that organizes the many research works and the different RL methods that use ER. Here, the focus is on how RL methods improve and apply ER strategies, demonstrating their specificities and contributions while having ER as a prominent component. Another relevant contribution is the organization in a facet-oriented way, allowing different perspectives of reading, whether based on the fundamental problems of RL, focusing on algorithmic strategies and architectural decisions, or with a view to different applications of RL with ER. Moreover, we start by presenting a detailed formal theoretical foundation of RL and some of the most relevant algorithms and bring from the recent literature some of the main trends, challenges, and advances focused on ER formal basement and how to improve its propositions to make it even more efficient in different methods and domains. Lastly, we discuss challenges and open problems and present relevant paths to feature works.