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Transfer Learning with Reinforcement Learning

Transfer Learning with Reinforcement Learning (Transfer RL) is a training paradigm in which knowledge acquired by an agent in one or more source tasks — encoded as policy weights, value functions, or learned representations — is reused to accelerate or improve learning in a related but different target task. It directly addresses the sample-inefficiency that plagues reinforcement learning from scratch in complex or expensive environments.

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Transfer Learning Applied to Reinforcement Learning

Taksonomski zapis metode · ml-model / deep-learning

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Taxonomic bucketDomain-adaptive reinforcement learningmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketFine-Tuned Reinforcement Learningmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketReinforcement Learningmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketTransfer Learning with Convolutional Neural Networkmachine-suggested · Relational suggestion, not evidence.