Some Insights into Lifelong Reinforcement Learning Systems [article]

Changjian Li
<span title="2020-01-27">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A lifelong reinforcement learning system is a learning system that has the ability to learn through trail-and-error interaction with the environment over its lifetime. In this paper, I give some arguments to show that the traditional reinforcement learning paradigm fails to model this type of learning system. Some insights into lifelong reinforcement learning are provided, along with a simplistic prototype lifelong reinforcement learning system.
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2001.09608v1">arXiv:2001.09608v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/f56hobcawfbbfnxsm3dtscmduy">fatcat:f56hobcawfbbfnxsm3dtscmduy</a> </span>
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