Deep reinforcement learning (DRL) has shown remarkable success in simulation domains, yet its application in designing robot controllers remains limited, due to its single-task orientation and insufficient adaptability to environmental changes. To overcome these limitations, we present a novel adaptive agent that leverages transfer learning techniques to dynamically adapt policy in response to different tasks and environmental conditions. The approach is validated through the blimp control challenge, where multitasking capabilities and environmental adaptability are essential. The agent is trained using a custom, highly parallelized simulator built on IsaacGym. We perform zero-shot transfer to fly the blimp in the real world to solve various tasks. We share our code at \url{https://github.com/robot-perception-group/adaptive\_agent/}.

翻译：深度强化学习在模拟环境中取得了显著成功，但其在机器人控制器设计中的应用仍受到限制，主要由于它面向单一任务且对环境变化的适应性不足。为克服这些局限，我们提出一种新颖的自适应智能体，该智能体利用迁移学习技术动态调整策略以应对不同任务和环境条件。该方法通过飞艇控制这一挑战性任务进行了验证，其中多任务能力和环境适应性至关重要。该智能体基于IsaacGym搭建的定制化高度并行模拟器进行训练，并实现零样本迁移以在真实世界中操控飞艇完成多种任务。我们已在\url{https://github.com/robot-perception-group/adaptive\_agent/} 开源相关代码。