actions array 3D	actions_onehot array 3D	avail_actions array 3D	filled array 2D	obs array 3D	reward array 2D	state array 2D	terminated array 2D
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SMACv2 Offline Multi-Agent RL Datasets

Offline reinforcement-learning datasets collected on the SMACv2 (StarCraft Multi-Agent Challenge v2) benchmark. Trajectories were generated by QMIX policies and stored as episode batches in HDF5 format, suitable for offline MARL research.

Scenarios

Three races × three team sizes:

Race	Team sizes
Protoss	3v3, 5v5, 10v10
Terran	3v3, 5v5, 10v10
Zerg	3v3, 5v5, 10v10

For each scenario two dataset qualities are provided:

medium — trajectories from a mid-level (partially trained) policy.
medium-replay — the replay buffer accumulated while training up to the medium level (a mixture of behaviors of varying quality).

Directory layout

<race>_<n>_vs_<n>/
  medium/<run>/part_*.h5
  medium-replay/<run>/part_*.h5

File format

Each .h5 file holds a batch of episodes with the following datasets, shaped (n_episodes, max_seq_len, n_agents, ...):

Key	Shape (example, 3v3)	dtype	Description
`obs`	`(2000, 201, 3, 56)`	float32	Per-agent local observations
`state`	`(2000, 201, 72)`	float32	Global state
`actions`	`(2000, 201, 3, 1)`	int64	Chosen action indices
`actions_onehot`	`(2000, 201, 3, 9)`	float32	One-hot encoded actions
`avail_actions`	`(2000, 201, 3, 9)`	int32	Available-action mask
`reward`	`(2000, 201, 1)`	float32	Shared team reward
`terminated`	`(2000, 201, 1)`	uint8	Episode-termination flag
`filled`	`(2000, 201, 1)`	int64	Valid-timestep mask (padding indicator)

Observation/state/action dimensions and agent counts vary by scenario.

Usage

import h5py
from huggingface_hub import hf_hub_download

path = hf_hub_download(
    repo_id="jwjeonn/smacv2-offline",
    filename="protoss_3_vs_3/medium/qmix_2025-11-14_13-14-58/part_0.h5",
    repo_type="dataset",
)

with h5py.File(path, "r") as f:
    obs = f["obs"][:]          # (n_episodes, max_seq_len, n_agents, obs_dim)
    reward = f["reward"][:]
    print(obs.shape, reward.shape)

To pull the whole dataset:

from huggingface_hub import snapshot_download
snapshot_download(repo_id="jwjeonn/smacv2-offline", repo_type="dataset")

License

Released under CC-BY-4.0. The underlying SMACv2 environment is maintained by the WhiRL group; please also cite SMACv2 if you use these datasets.

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Total file size:

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