  Recent advancements in imitation learning have been largely fueled by the
integration of sequence models, which provide a structured flow of information
to effectively mimic task behaviours. Currently, Decision Transformer (DT) and
subsequently, the Hierarchical Decision Transformer (HDT), presented
Transformer-based approaches to learn task policies. Recently, the Mamba
architecture has shown to outperform Transformers across various task domains.
In this work, we introduce two novel methods, Decision Mamba (DM) and
Hierarchical Decision Mamba (HDM), aimed at enhancing the performance of the
Transformer models. Through extensive experimentation across diverse
environments such as OpenAI Gym and D4RL, leveraging varying demonstration data
sets, we demonstrate the superiority of Mamba models over their Transformer
counterparts in a majority of tasks. Results show that HDM outperforms other
methods in most settings. The code can be found at
https://github.com/meowatthemoon/HierarchicalDecisionMamba.
