 However, a major drawback of using DRL is the Data hungry training regime of DRL that requires millions of trial and error attempts, impractical in real robotic hardware. We propose a multi-subtask reinforcement learning method where complex tasks can be decomposed into low-level subtasks  .
<clarity>  However, a major drawback of using DRL is the Data hungry training regime of DRL that requires millions of trial and error attempts, impractical in real robotic hardware. We propose a multi-subtask reinforcement learning method where complex tasks can be decomposed into low-level subtasks  .
 However, a major drawback of using DRL is the Data hungry training regime of DRL that requires millions of trial and error attempts, which is impractical when running experiments on robotic systems. To address this issue, we propose a multi-subtask reinforcement learning method where complex tasks can be decomposed into low-level subtasks .