language:
- en
tags:
- mcd
- animal-behavior
- motor-motifs
- reinforcement-learning
pretty_name: MCD supplementary replay buffers
MCD supplementary replay buffers
This dataset hosts five large serialized data files used with the MCD code repository. It includes four artifacts from the supplied supplementary code package and the free-moving training buffer from the development workspace, all copied unchanged. They are research replay-buffer artifacts, not a tabular dataset.
Files and original paths
| Path relative to the code repository | Bytes |
|---|---|
free-moving/kms/train_data_all.pth |
297,740,911 |
free-moving/kms/test_data_all.pth |
74,518,191 |
gridworld/model/RandomGridWorld-v0/spedersac/replay_buffer_VI_onehotsa.pkl |
30,718,255 |
labyrinth/model/labyrinth/spedersac/replay_buffer_labyrinth.pkl |
252,812,591 |
labyrinth/model/labyrinth/spedersac/replay_buffer_random_labyrinth.pkl |
191,970,927 |
The total size of these five files is 847,760,875 bytes.
data_manifest.json records byte sizes and SHA-256 checksums. Paths are preserved
so the files can be restored into the code repository without modifying scripts.
Download and verify
From a checkout of the code repository:
pip install huggingface_hub
python download_data.py
The downloader restores the expected paths and verifies SHA-256 checksums. The repository documentation describes the original Conda environment and experiment-specific launchers.
Scope and limitations
The free-moving buffers contain 515,120 training transitions and 128,781 test
transitions, with 16-dimensional states and 16-dimensional displacement actions.
The training buffer is read by main.py; the test buffer is read by
visualize.py. The source code and experiment-specific instructions are in the
GitHub repository.
This dataset does not include the later macaque drawing experiments. Some individual analysis routines may require additional generated artifacts; publishing these files does not establish end-to-end reproducibility of every analysis script.
These files use pickle or PyTorch serialization. Only deserialize files from a trusted source; checksum verification checks integrity, not execution safety. No new license is selected by this packaging step. Consult the authors regarding reuse permissions and any applicable source-data conditions.