SignalBench FrozenLake Dense Signal Dataset contains evaluation points, rendered state images, and Monte Carlo dense-signal labels under a scripted policy. The dataset includes synchronized views, with one executable artifact and a data file containing one row per benchmark example. It was created by neurips2026-anonymous and last updated on 2026-05-07.
Use Cases
- Benchmarking dense-signal reinforcement learning algorithms based on Monte Carlo labels
- Analyzing state transitions in grid-world environments based on state/action/next-state text
- Training or evaluating models that process rendered state images from the FrozenLake environment
Strengths
- Includes synchronized views of executable artifacts and example data
- Contains rendered state_image and next_state_image columns for visual representation
- Labels are generated via Monte Carlo methods under a scripted policy
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download
- Column-level documentation is absent; field semantics must be inferred after download
- Row count is unknown, which may limit suitability assessment
Provenance
- Source
- neurips2026-anonymous
- Collection Method
- Monte Carlo dense-signal labels generated under a scripted policy
- Freshness
- Last updated 2026-05-07 06:27:50