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.

深度强化学习代理人在适应训练分布之外的任务时面临着过拟合、灾难性遗忘和样本效率问题。本文探讨了适配器在强化学习中的应用，提出了一种创新的适应策略，在 nanoRTS 环境中实验，提高了训练效率并改进了基础代理人，同时兼容预训练神经网络和基于规则的代理人，提供了融合人类专业知识的方法。