Learning Robust Reward Machines from Noisy Labels

Roko Parac, Lorenzo Nodari, Leo Ardon, Daniel Furelos-Blanco, Federico Cerutti, Alessandra Russo

Abstract: This paper presents PROB-IRM, an approach that learns robust reward machines (RMs) for reinforcement learning (RL) agents from noisy execution traces. The key aspect of RM-driven RL is the explo...