Abstract
Transfer Learning (TL) offers the potential to accelerate learning by transferring knowledge across tasks. However, it faces critical challenges such as negative transfer, domain adaptation and inefficiency in selecting solid source policies. These issues often represent critical problems in evolving domains, i.e. game development, where scenarios transform and agents must adapt. The continuous release of new agents is costly and inefficient. In this work we challenge the key issues in TL to improve knowledge transfer, agents performance across tasks and reduce computational costs. The proposed methodology, called FAST - Framework for Adaptive Similarity-based Transfer, leverages visual frames and textual descriptions to create a latent representation of tasks dynamics, that is exploited to estimate similarity between environments. The similarity scores guides our method in choosing candidate policies from which transfer abilities to simplify learning of novel tasks. Experimental results, over multiple racing tracks, demonstrate that FAST achieves competitive final performance compared to learning-from-scratch methods while requiring significantly less training steps. These findings highlight the potential of embedding-driven task similarity estimations.
Abstract (translated)
迁移学习（TL）具有通过跨任务转移知识来加速学习的潜力。然而，它面临着诸如负向迁移、领域适应和在选择稳固源策略方面低效等关键挑战。这些问题往往代表了如游戏开发这类不断变化领域的关键问题，在这些领域中场景会转变且代理必须进行调整。新代理的持续推出既昂贵又效率低下。 在这项工作中，我们致力于解决TL中的核心问题以提高知识转移、跨任务代理性能，并减少计算成本。提出的名为FAST（基于自适应相似性的迁移框架）的方法利用视觉帧和文本描述来创建任务动态的潜在表示形式，进而用于估算环境间的相似性。这些相似度评分指导我们的方法选择候选策略从中学到迁移能力以简化新任务的学习过程。 实验结果显示，在多个赛车赛道上进行测试时，FAST实现了与从头开始学习方法相当甚至更好的最终性能，同时需要显著较少的训练步骤。这些发现突显了基于嵌入的任务相似性估算的潜力。
URL
https://arxiv.org/abs/2507.20433
