DistRL
Distributed online reinforcement-learning framework for Android agents, with environment setup and trajectory collection tools.
AI-assisted editorial review against a primary source. This is a catalog annotation, not an independent reproduction or endorsement.
What is this resource?
- Resource type
- Frameworks
- Environment
- android
- Topics
- reinforcement-learning, efficiency
- Record publication date
- Not recorded
- Evidence retrieved
- 2026-10-16T10:42:12Z
- Resource ID
- github:distrl-lab/distrl-open
What licenses are recorded?
Code, training data, weights, and documents can have different terms. A blank declaration means unknown, not unrestricted use.
- Code
- Apache-2.0 (repository LICENSE header; not a weights/data license)
- Data
- Not recorded
- Document
- Not recorded
- Weights
- Not recorded
Explore related topics
Scope and limitations
The README identifies model weights and dataset release as pending; this entry covers the code framework.
Related resources
- documented by: DistRL: An Asynchronous Distributed Reinforcement Learning Framework for On-Device Control Agents
Primary evidence and verification
Source retrieval is separate from experiment reproduction. Use the original publication or publisher page when citing claims about the resource.
- editorial primary review
Retrieved 2026-10-16T10:42:12Z. Supports: name, kind, summary, environments, topics, scope, relations.
Content fingerprint
f501f8de6aac53758f60882a8c595a5ac27145f327c73b0cfa7921b45ccae75a - license header
Retrieved 2026-10-16T10:48:41Z. Supports: licenses.code.
Content fingerprint
c71d239df91726fc519c6eb72d318ec65820627232b2f796219e87dcf35d0ab4
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