Jonathan Hau

Efficient Bayes-Adaptive Reinforcement Learning with Temporal Logic Specifications

A novel end-to-end model-based Reinforcement Learning (RL) algorithm for efficient policy synthesis under given Linear Temporal Logic (LTL) specifications in unknown environments is presented and a successful application is showcased for \textit{cautious} RL, namely to reduce the number of task violations incurred duri...