Arxiv paper - DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning

In this episode, we discuss DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning by Hao Bai, Yifei Zhou, Mert Cemri, Jiayi Pan, Alane Suhr, Sergey Levine, Aviral Kumar. DigiRL is an innovative autonomous reinforcement learning approach designed to train device control agents by refining pre-trained vision language models through a two-stage process involving offline RL and offline-to-online RL. It addresses traditional VLM limitations by introducing enhanced advantage estimators and an automatic curriculum to optimize learning in a scalable Android environment. Experiments on the Android-in-the-Wild dataset showed that DigiRL significantly outperformed existing methods, setting a new standard in device control tasks.