  Decision Transformers, in their vanilla form, struggle to perform on
image-based environments with multi-discrete action spaces. Although enhanced
Decision Transformer architectures have been developed to improve performance,
these methods have not specifically addressed this problem of multi-discrete
action spaces which hampers existing Decision Transformer architectures from
learning good representations. To mitigate this, we propose Multi-State Action
Tokenisation (M-SAT), an approach for tokenising actions in multi-discrete
action spaces that enhances the model's performance in such environments. Our
approach involves two key changes: disentangling actions to the individual
action level and tokenising the actions with auxiliary state information. These
two key changes also improve individual action level interpretability and
visibility within the attention layers. We demonstrate the performance gains of
M-SAT on challenging ViZDoom environments with multi-discrete action spaces and
image-based state spaces, including the Deadly Corridor and My Way Home
scenarios, where M-SAT outperforms the baseline Decision Transformer without
any additional data or heavy computational overheads. Additionally, we find
that removing positional encoding does not adversely affect M-SAT's performance
and, in some cases, even improves it.
