Lifelong reinforcement learning is a research paradigm in artificial intelligence focused on developing agents capable of learning continuously from a stream of diverse tasks or interacting with changing environments over an extended period. This field investigates methodologies for agents to effectively acquire, retain, and transfer knowledge and skills learned from past experiences to facilitate more efficient and robust learning in future, potentially novel, situations, while mitigating issues like catastrophic forgetting. Its significance lies in advancing the development of general-purpose intelligent agents that can adapt and perform effectively in dynamic, open-ended domains through cumulative and persistent learning, leveraging a growing repository of knowledge.