Unsupervised Representation Learning in Deep Reinforcement Learning: A Review
It addresses the need for better representation learning in DRL to enhance performance and understanding, but it is incremental as it reviews existing work rather than introducing new methods.
This review tackles the problem of learning abstract representations from ambiguous, high-dimensional data in Deep Reinforcement Learning to improve data efficiency, robustness, and interpretability, providing a comprehensive overview of methods, applications, and challenges.
This review addresses the problem of learning abstract representations of the measurement data in the context of Deep Reinforcement Learning (DRL). While the data are often ambiguous, high-dimensional, and complex to interpret, many dynamical systems can be effectively described by a low-dimensional set of state variables. Discovering these state variables from the data is a crucial aspect for (i) improving the data efficiency, robustness, and generalization of DRL methods, (ii) tackling the curse of dimensionality, and (iii) bringing interpretability and insights into black-box DRL. This review provides a comprehensive and complete overview of unsupervised representation learning in DRL by describing the main Deep Learning tools used for learning representations of the world, providing a systematic view of the method and principles, summarizing applications, benchmarks and evaluation strategies, and discussing open challenges and future directions.