ADAPTER-RL: Adaptation of Any Agent using Reinforcement Learning
This addresses adaptation challenges for DRL agents in tasks outside their training distribution, offering a universal approach compatible with various agent types.
The paper tackled the problem of deep reinforcement learning agents struggling to adapt to new tasks by integrating adapters, a technique from supervised learning, into reinforcement learning, resulting in improved training efficiency and base-agent performance in the nanoRTS environment.
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.