Deep Reinforcement Learning (DRL) agents frequently face challenges in adapting to tasks outside their training distribution, including issues with over-fitting, catastrophic forgetting and sample inefficiency. Although the application of adapters has proven effective in supervised learning contexts such as natural language processing and computer vision, their potential within the DRL domain remains largely unexplored. This paper delves into the integration of adapters in reinforcement learning, presenting an innovative adaptation strategy that demonstrates enhanced training efficiency and improvement of the base-agent, experimentally in the nanoRTS environment, a real-time strategy (RTS) game simulation. Our proposed universal approach is not only compatible with pre-trained neural networks but also with rule-based agents, offering a means to integrate human expertise.

翻译：摘要：深度强化学习（DRL）智能体在适应其训练分布外的任务时常面临挑战，包括过拟合、灾难性遗忘和样本效率低下等问题。尽管在自然语言处理和计算机视觉等监督学习场景中，适配器的应用已被证明有效，但其在深度强化学习领域的潜力仍未得到充分探索。本文深入研究了适配器在强化学习中的集成，提出了一种创新的自适应策略，在实时策略（RTS）游戏仿真环境nanoRTS中实验验证了该策略对基础智能体的训练效率提升与性能增强。我们提出的通用方法不仅兼容预训练神经网络，也适用于基于规则的智能体，为人类专业知识的整合提供了可行途径。