Browser agents are the use case where RL makes the most sense - the reward signal is obvious (did the task get done or not) and the action space is bounded. Curious how you handle the credit assignment problem across multi-step navigation though.

Interesting, how do you handle the observability side during training? One thing I ran into with multi-agent RL is that reward signals alone don't tell you much about why an agent is failing. Curious if you've built any tooling around that.