mindrl-hub/mindrl-challenge-public

MindRL Challenge Public Dataset

This repository contains the public training split for the MindRL Challenge, a human reward-learning modeling benchmark. The release is intentionally source-safe: it describes the task, data format, and intended use without identifying the original study, raw data source, article, repository, lab, institution, or participant pool.

Intended use

Use this public split to develop, debug, and calibrate MindRL agents that predict human choices from trial history. Official challenge scoring uses additional private evaluation splits that are not part of this public release.

The modeling target is one-step-ahead behavioral prediction:

P(action_t | context, history_1:t-1)

At prediction time, models should use only the trajectory context and previous trials. They should not use the current trial's action or reward, later trials from the same trajectory, hidden splits, or any external reconstruction of the raw data source.

Task

• Task family: 4-arm drifting bandit / restless reward-learning.
• Action space: integers 0, 1, 2, 3.
• Observation history: previous choices, received rewards, response times when available, and anonymized episode metadata.
• Prediction target: the human participant's next action.
• Episode unit: each JSONL row is one anonymized trajectory.
• The reward associated with each action changes gradually over the episode, so useful models need to learn from recent outcomes while remaining responsive to change.

Files

File	Purpose
public_train.jsonl	Agent-facing public training trajectories.
public_train_reward_schedules.jsonl	Full option payoff schedules for released public episodes; useful for public-task analysis or generative simulation, but not part of the standard one-step-ahead observation history.
schema.json	JSON Schema for public trajectories.
task_description.md	Compact task description.
README.md / dataset_card.md	This dataset description.

If a model uses the reward-schedule sidecar, that use should be disclosed in the method description or interpretation card. The official prediction API still evaluates one-step-ahead predictions from context and past observed trials.

Public split statistics

These statistics describe public_train.jsonl only.

Quantity	Value
Trajectories	2,678
Anonymized subjects	689
Anonymized blocks / episodes	2,678
Total trials	320,080
Trajectory length, min / median / max	108 / 120 / 120
Reward range	1.0 to 100.0
Mean reward	64.889
Trials with response time	308,803

Observed action counts:

Action	Count
0	81,656
1	81,698
2	77,045
3	79,681

These counts should not be interpreted as the full challenge distribution.

JSONL format

Each line in public_train.jsonl is one trajectory:

{
  "context": {
    "task_id": "public_4arm_restless_bandit",
    "task_family": "restless_bandit",
    "subject_id": "sub_000001",
    "trajectory_id": "traj_000001",
    "num_options": 4,
    "available_actions": [0, 1, 2, 3],
    "option_features": null,
    "task_description": "4-arm drifting bandit task",
    "metadata": {"block_id": "block_000001"}
  },
  "trials": [
    {"trial_index": 0, "action": 1, "reward": 72.0, "info": {"rt": 531.0}}
  ]
}

Context fields

Field	Meaning
task_id	Public task identifier: public_4arm_restless_bandit.
task_family	Coarse task family: restless_bandit.
subject_id	Anonymized subject label such as sub_000001; not an original participant id.
trajectory_id	Anonymized trajectory label such as traj_000001.
num_options	Number of available actions.
available_actions	Valid actions, encoded as integers [0, 1, 2, 3].
option_features	Optional action-feature field; null in this release.
task_description	Short task description string.
metadata.block_id	Anonymized episode/block label; not the original block number.

Trial fields

Field	Meaning
trial_index	Within-trajectory trial index after preprocessing.
action	Observed human action, encoded as 0, 1, 2, or 3.
reward	Scalar reward feedback received after the action.
info.rt	Response time when available. Some trials may have missing response time.

Loading examples

Download the public trajectory file:

huggingface-cli download mindrl-hub/mindrl-challenge-public public_train.jsonl --local-dir ./hf_cache/public

Load with Python JSONL:

import json
from pathlib import Path

path = Path("hf_cache/public/public_train.jsonl")
with path.open("r", encoding="utf-8") as f:
    first = json.loads(next(f))

print(first["context"]["available_actions"], len(first["trials"]))

Load with datasets:

from datasets import load_dataset

ds = load_dataset(
    "json",
    data_files="https://huggingface.co/datasets/mindrl-hub/mindrl-challenge-public/resolve/main/public_train.jsonl",
    split="train",
)
print(ds[0])

Evaluation interface

The official evaluator calls an Agent sequentially:

1. reset(context) once at the start of a trajectory.
2. For each trial, call predict(history) using only previous trials.
3. Score the predicted action distribution against the observed human action.
4. Call update(action, reward, info) after the true trial outcome is revealed.
5. Append the trial to history and continue.

A valid prediction returns probabilities over the available actions, for example:

{"action_probs": {0: 0.25, 1: 0.25, 2: 0.25, 3: 0.25}}

Safety and leakage policy

• Original participant identifiers, original block numbers, original trial ids, raw filenames, and raw study/source identifiers are not included.
• Demographic variables are not included.
• subject_id, trajectory_id, and metadata.block_id are anonymized labels.
• Counterfactual option payoffs are not included in the agent-facing trajectory history. For public episodes, they are kept only in the separate reward-schedule sidecar.
• The dataset card intentionally omits the original article and raw-data source so that challenge participants cannot trivially recover hidden evaluation splits from external copies of the raw dataset.

Do not use this release to identify the original study, raw dataset, collection site, institution, lab, article, or participant pool. Do not add source-identifying details to public repositories, participant documentation, or submissions.

Relationship to hidden evaluation

The public split supports development. Official challenge evaluation uses hidden splits prepared by the organizers to test generalization across unreleased trials, unreleased participants, unreleased episodes, and a separate held-out task. Hidden evaluation files and source mappings are not part of this public release.