In the current release, the dynamics model and model-free methods use the same published processed dataset rather than separate raw/ and processed/ public splits.

Supported Tasks

The repository defines the following canonical control tasks:

temp
rotation
dens
pres
q
betan

Data Organization

Directory layout

data/
└─ processed/
   ├─ full.hdf5
   ├─ il_data.h5
   ├─ info.pkl
   ├─ rl_data.h5
   ├─ tracking_test.h5
   ├─ tracking_val.h5
   └─ used_shots_info.txt

Role of the published `processed/` split

The published release is centered on processed/, which is the split intended for downstream use.

These processed files are the ones typically used by:

offline RL training
imitation learning
dynamics model training and related model-based pipelines
held-out tracking validation and test evaluation

In particular, the current benchmark setup uses the same released processed dataset for both dynamics-model training and model-free offline RL experiments.

Main processed files

`processed/full.hdf5`

This is the broadest processed transition store. It contains:

states: (945828, 26)
next_states: (945828, 26)
actuators: (945828, 13)
next_actuators: (945828, 13)
shotnum: (945828,)
time: (945828,)
actuator and state bounds

This file is useful if you want the main transition stream with shot IDs and timestamps.

`processed/rl_data.h5`

This file is the main training dataset used by the benchmark. It contains:

observations: (849977, 26)
next_observations: (849977, 26)
actions: (849977, 13)
pre_actions: (849977, 13)
terminals
time_step
traj_start_indices
hidden_states: (849977, 25, 1, 256)
action/state bounds

It is the principal dataset for model-free offline RL and also the aligned released dataset for dynamics-model-related workflows in the current public release.

`processed/il_data.h5`

This file is the imitation learning counterpart of rl_data.h5. It contains the same core transition fields, but does not include the RNN dynamics hidden_states.

`processed/tracking_val.h5` and `processed/tracking_test.h5`

These files are grouped by shot ID. Each top-level key is a shot number, and each shot contains:

tracking_states: (T, 26)
tracking_next_states: (T, 26)
tracking_pre_actions: (T, 13)
tracking_actions: (T, 13)

Statistics:

tracking_val.h5: 300 shots, sequence length range 130 to 180, average 160.03
tracking_test.h5: 300 shots, sequence length range 130 to 180, average 159.47

These files are intended for rollout-style evaluation and profile tracking experiments.

Feature Description

State space

The observation/state dimension is 26. According to processed/info.pkl, the state variables are:

betan_EFIT01
dssdenest
li_EFIT01
n1rms
vloop
wmhd_EFIT01
temp_component1 to temp_component4
itemp_component1 to itemp_component4
dens_component1 to dens_component4
rotation_component1 to rotation_component4
pres_EFIT01_component1 to pres_EFIT01_component2
q_EFIT01_component1 to q_EFIT01_component2

The corresponding next_state_space stored in metadata is expressed as state velocities or deltas for the same 26 channels.

Action space

The action dimension is 13. According to processed/info.pkl, the actuator channels are:

pinj
tinj
ipsiptargt
bt_magnitude
bt_is_positive
gasA
aminor_EFIT01
tritop_EFIT01
tribot_EFIT01
kappa_EFIT01
rmaxis_EFIT01
zmaxis_EFIT01
ech_pwr_total

The next_actuator_space corresponds to actuator velocities or deltas for the same 13 channels.

How To Load

These files are distributed as HDF5 rather than Arrow or Parquet, so the easiest way to inspect them is with h5py.

import h5py

with h5py.File("processed/rl_data.h5", "r") as f:
    print(f["observations"].shape)
    print(f["actions"].shape)
    print(f["hidden_states"].shape)

For tracking data:

import h5py

with h5py.File("processed/tracking_test.h5", "r") as f:
    shot_ids = list(f.keys())
    shot = shot_ids[0]
    print(shot)
    print(f[shot]["tracking_states"].shape)
    print(f[shot]["tracking_actions"].shape)

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Fusion RL Benchmark

Dataset Summary