Create README.md

README.md

@@ -1,19 +1,101 @@
 ---
-license: apache-2.0
-task_categories:
-- reinforcement-learning
-- other
-language:
-- code
-tags:
-- crafter
-- craftax
-- expert-demonstrations
-- skill-segmentation
-- action-segmentation
-- object-centric
-pretty_name: Craftax Skill Segmentation Dataset
-size_categories:
-- 1K<n<10K
-num_of_elements: 2354
+# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
+{}
 ---
+
+# Dataset Card for Craftax Expert Skill Data
+This dataset consists of expert demonstration trajectories from the Crafter (Craftax) environment. Each trajectory includes ground-truth skill segmentation annotations, enabling research into action segmentation, skill discovery, imitation learning, and reinforcement learning with temporally-structured data.
+
+## Dataset Details
+
+### Dataset Description
+
+The **Craftax Skill Segmentation Dataset** contains gameplay trajectories from an expert policy in the Crafter environment. Each trajectory is labeled with ground-truth skill boundaries and skill identifiers, allowing users to train and evaluate models for temporal segmentation, behavior cloning, and skill-based representation learning.
+
+- **Curated by:** Damio
+- **License:** Apache 2.0
+- **Language(s) (NLP):** Not applicable (code/visual)
+
+### Dataset Sources
+
+- **Repository:** https://huggingface.co/datasets/dami2106/Craftax-Skill-Data
+- **Environment:** [CraftAX](https://craftaxenv.github.io/) 
+
+## Uses
+
+### Direct Use
+
+This dataset is designed for use in:
+- Training models to segment long-horizon behaviors into reusable skills.
+- Evaluating action segmentation or hierarchical RL approaches.
+- Studying object-centric or spatially grounded RL methods.
+- Pretraining representations from visual expert data.
+
+### Out-of-Scope Use
+
+- Language-based tasks (no natural language data is included).
+- Real-world robotics (simulation-only data).
+- Tasks requiring raw image pixels if they are not included in your setup.
+
+## Dataset Structure
+
+Each data file includes:
+- A sequence of states (top-down pixels, symbolic, pixel-obs, or PCA features).
+- Corresponding actions (no-op action appended for final state).
+- Skill labels marking where each skill begins and ends.
+
+Example structure:
+Example structure:
+```json
+{
+  "task_id": "name of the task",
+  "pixel_obs": [...],         // Raw visual observations (e.g., RGB frames)
+  "top_down_obs": [...],      // Environment state from a top-down view
+  "pca_features": [...],      // Compressed feature vectors 
+  "actions": [...],           // Agent actions
+  "groundTruth": [...],       // Ground-truth skill segmentation labels
+  "mapping":                // Mapping metadata for skill ID -> groundTruth
+
+}
+```
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset was created to support research in skill discovery and temporal abstraction in visual reinforcement learning environments. The Craftax environment provides meaningful high-level tasks and object interactions, making it a suitable benchmark.
+
+### Source Data
+
+#### Data Collection and Processing
+
+- The expert trajectories were generated using a scripted or trained expert policy in the Craftax environment.
+- Skill labels were annotated using environment signals (e.g., task success or inventory changes) and manual rules.
+
+#### Who are the source data producers?
+
+The data was generated programmatically in the Craftax simulation environment by an expert agent.
+
+### Annotations
+
+#### Annotation process
+
+Skill annotations were added using heuristics based on inventory changes, environment triggers, and task events. They were verified for consistency across episodes.
+
+#### Who are the annotators?
+
+Automated heuristics with manual inspection during development.
+
+
+
+## Bias, Risks, and Limitations
+
+- The dataset is based on simulation, so real-world transferability may be limited.
+- Skill labels are heuristically defined, which may not reflect a true underlying skill taxonomy.
+- The expert behavior might be biased toward one specific strategy or task order.
+
+### Recommendations
+
+Researchers should consider validating learned skills on diverse evaluation tasks. Skill segmentation boundaries are approximations and might not generalize well to different agents or environments.
+