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Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains

Description

Transfer learning in Reinforcement Learning (RL) enables agents to leverage knowledge from source tasks to accelerate learning in target tasks. While prior work, such as the Attend, Adapt, and Transfer (A2T) framework, addresses negative transfer and selective transfer, other critical challenges remain underexplored. This paper introduces the Generalized Adaptive Transfer Network (GATN), a deep RL architecture designed to tackle task generalization across domains, robustness to environmental cha

Source

http://arxiv.org/abs/2507.03026v1