Towards Integrating Abstraction and Partial Order Reduction in Probabilistic Model Checking: Survey of Challenges and Opportunities

Abstract

Long Short-Term Memory (LSTM) architectures have recently seen significant advancements through innovations such as exponential gating and modified memory structures, reigniting interest in their potential for modern sequence-based tasks. While xLSTM models have demonstrated strong performance in language modeling, their suitability for reinforcement learning (RL) tasks has yet to be fully explored. In this work, we investigate the application of xLSTM in RL environments, focusing on classic control tasks tasks that are commonly employed as benchmarks. This comparison provides a starting point for understanding the differences between xLSTM and LSTM in the context of reinforcement learning.