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Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2. (arXiv:2208.05056v2 [cs.LG] UPDATED)
Aug. 17, 2022, 1:11 a.m. | Zachary Daniels, Aswin Raghavan, Jesse Hostetler, Abrar Rahman, Indranil Sur, Michael Piacentino, Ajay Divakaran
cs.LG updates on arXiv.org arxiv.org
One approach to meet the challenges of deep lifelong reinforcement learning
(LRL) is careful management of the agent's learning experiences, to learn
(without forgetting) and build internal meta-models (of the tasks,
environments, agents, and world). Generative replay (GR) is a biologically
inspired replay mechanism that augments learning experiences with self-labelled
examples drawn from an internal generative model that is updated over time. We
present a version of GR for LRL that satisfies two desiderata: (a)
Introspective density modelling of the …
application arxiv free learning lg reinforcement reinforcement learning
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