Stanford team proposed RAGEN-2, using mutual information regularizer to address the action stagnation problem in RL agents

ME News Report, April 9th (UTC+8), recently, a study called RAGEN-2 pointed out that although agents trained through reinforcement learning appear to exhibit diverse behaviors, in reality, they are merely repeating templates, resulting in high entropy but nearly zero mutual information, meaning the model has learned to talk nonsense in various ways. To address this issue, the researchers proposed an mutual information-aware regularizer. This study was jointly conducted by @wzenus, @ManlingLi_, @YejinChoinka, and Fei-Fei Li. (Source: InFoQ)