Awesome

Decision Mamba

Reinforcement Learning via Sequence Modeling with Selective State Spaces<br> https://arxiv.org/abs/2403.19925

Architecture

Below is the overview of our main module, the Mamba layer:

We adopt the basic Transformer-type neural network architecture for the Mamba layer, namely it consists of the token-mixing block and the channel-mixing block. The right-hand side of the figure illustrates the series of operations performed inside the Mamba block. $\sigma$ is the $\mathrm{SiLU}$ activation function and $\odot$ denotes the element-wise product. For more details, see Section 3 of the paper.

Instructions

We provide the corresponding code in two sub-directories: atari containing code for Atari experiments and gym containing code for OpenAI Gym experiments. See corresponding READMEs in each folder for instructions. Scripts should be run from the respective directories.

Acknowledgements

Our code largely relies on the implementations of decision-transformer and mamba. We thank their excellent works.

Citation

If you use our code, or otherwise found our work useful, please cite the accompanying paper:

@article{ota2024decision,
    title   = {Decision Mamba: Reinforcement Learning via Sequence Modeling with Selective State Spaces},
    author  = {Toshihiro Ota},
    journal = {arXiv preprint arXiv:2403.19925},
    year    = {2024}
}