<ul data-eligibleForWebStory="true"><li>Reinforcement Learning (RL) has seen progress in solving decision-making problems, leveraging deep neural networks.</li><li>Continual Reinforcement Learning (CRL) has emerged to address RL limitations by enabling continuous learning and adaptation to new tasks.</li><li>A survey on CRL covers core concepts, challenges, and methodologies, reviewing existing works and proposing a new taxonomy of CRL methods.</li><li>The analysis underscores the unique challenges of CRL and offers insights for future research directions in this field.</li></ul>

A Survey of Continual Reinforcement Learning

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