Exploring History-Aware Online Actor-Critic for Smart Manufacturing Tasks in the RICAIP Testbed

Abstract

As manufacturing capabilities advance to greater autonomy, interest is increasingly directed toward versatile agents capable of performing complex tasks. Recently, learning-based approaches have shown more rapid progress compared to classical methods. While these advancements are enabled by the offline setting of Imitation Learning (IL), transfer to pure online exploration Reinforcement Learning (RL) remains less explored. This work experiments with a simple extension to the standard Markovian MLP policy by explicitly encoding a history of states using a tiny transformer model.