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Lifelong Incremental Reinforcement Learning with Online Bayesian Inference
[article]
2020
arXiv
pre-print
A central capability of a long-lived reinforcement learning (RL) agent is to incrementally adapt its behavior as its environment changes, and to incrementally build upon previous experiences to facilitate future learning in real-world scenarios. In this paper, we propose LifeLong Incremental Reinforcement Learning (LLIRL), a new incremental algorithm for efficient lifelong adaptation to dynamic environments. We develop and maintain a library that contains an infinite mixture of parameterized
arXiv:2007.14196v1
fatcat:nrmsbnscvben3hiwch7bm6r4lm