Andreas Krause

Scalable, simple, and practical algorithm for model-based RL with regret bounds across several RL settings and experiments on state-based, visual control and hardware tasks.

We present SonoGym, a scalable simulation platform for challenging robotic ultrasound tasks that allows training reinforcement learning and imitation learning agents.

We develop DISCOVER, which enables RL agents to solve substantially more challenging tasks than previous exploration strategies in RL.

We develop a practical algorithm for safe sim-to-real transfer