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

2 Jul 2025arXiv:2507.03026archive 2025-07-28

Abhishek Verma, Nallarasan V, Balaraman Ravindran

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 changes, and computational efficiency in transfer. GATN employs a domain-agnostic representation module, a robustness-aware policy adapter, and an efficient transfer scheduler to achieve these goals. We evaluate GATN on diverse benchmarks, including Atari 2600, MuJoCo, and a custom chatbot dialogue environment, demonstrating superior performance in cross-domain generalization, resilience to dynamic environments, and reduced computational overhead compared to baselines. Our findings suggest GATN is a versatile framework for real-world RL applications, such as adaptive chatbots and robotic control.

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Tasks

Atari Games Chatbot Computational Efficiency Deep Learning Deep Reinforcement Learning Domain Generalization MuJoCo Reinforcement Learning Reinforcement Learning (RL)Transfer Learning

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Atari Games	Atari Pong	GATN-TL-Atari Pong	Total Reward	-0.24704	#1 of 1	Archive leaderboard	report

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