How Are Learned Perception-Based Controllers Impacted by the Limits of Robust Control?
We show empirically that the sample complexity and asymptotic performance of learned non-linear controllers in partially observable settings continues to follow theoretical limits based on the difficulty of state estimation
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings
How to train RL agents safely? We propose to pretrain a model-based agent in a mix of sandbox environments, then plan pessimistically when finetuning in the target environment.