Abstract
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{this https URL\_agent/}.
Abstract (translated)
深度强化学习（DRL）在模拟领域取得了显著的成功，但在设计机器人控制器方面，其应用仍然有限，因为其单任务导向和对于环境变化的适应性不足。为了克服这些限制，我们提出了一种新型的自适应机器人，它利用迁移学习技术动态地适应策略以应对不同的任务和环境条件。通过在鸡舍控制挑战中进行验证，多任务能力和环境适应性对这种方法至关重要。该机器人使用基于IsaacGym的自定义高度并行化的模拟器进行训练。我们在各种任务上通过零散转移控制飞艇在现实世界中解决各种问题。我们的代码存储在\url{这个 https://this URL\_agent/} 这个网站上。
URL
https://arxiv.org/abs/2404.18713
